{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient flow through targets of canned losses in torch.nn module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "\n",
    "BATCH_SIZE = 16\n",
    "\n",
    "criterion_mse = nn.MSELoss()\n",
    "x = Variable(torch.FloatTensor( BATCH_SIZE , 10  )  )\n",
    "l = nn.Linear( 10 , 10 )\n",
    "y = l(x)\n",
    "loss = criterion_mse( y, x )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading large csv file with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131072"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv, sys\n",
    "\n",
    "csv.field_size_limit(sys.maxsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cities_df = pd.read_csv(\"/home/data/world-cities/cities_over_10kpop.csv\", engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>city</th>\n",
       "      <th>province</th>\n",
       "      <th>country</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>geometry</th>\n",
       "      <th>City</th>\n",
       "      <th>area</th>\n",
       "      <th>scale</th>\n",
       "      <th>location</th>\n",
       "      <th>region</th>\n",
       "      <th>subregion</th>\n",
       "      <th>population</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>mandera</td>\n",
       "      <td>kenya</td>\n",
       "      <td>ken</td>\n",
       "      <td>POLYGON ((40.89144897460943 2.86349463462841, ...</td>\n",
       "      <td>1, mandera, ken</td>\n",
       "      <td>1374.518561</td>\n",
       "      <td>69.304028</td>\n",
       "      <td>POINT (40.61415008564921 2.994901880312582)</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>18861.0</td>\n",
       "      <td>very-small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1 decembrie</td>\n",
       "      <td>ilfov</td>\n",
       "      <td>romania</td>\n",
       "      <td>rou</td>\n",
       "      <td>POLYGON ((26.12038612365728 44.24510955810553,...</td>\n",
       "      <td>1 decembrie, ilfov, rou</td>\n",
       "      <td>27.143648</td>\n",
       "      <td>8.079587</td>\n",
       "      <td>POINT (26.07554312747147 44.28385681821968)</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>19797.0</td>\n",
       "      <td>very-small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>15 mayu</td>\n",
       "      <td>al qahirah</td>\n",
       "      <td>egypt</td>\n",
       "      <td>egy</td>\n",
       "      <td>POLYGON ((31.37738609313976 29.78891563415533,...</td>\n",
       "      <td>15 mayu, al qahirah, egy</td>\n",
       "      <td>81.015843</td>\n",
       "      <td>14.286717</td>\n",
       "      <td>POINT (31.37155071625964 29.85801900604489)</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>95957.0</td>\n",
       "      <td>very-small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>trans nzoia</td>\n",
       "      <td>kenya</td>\n",
       "      <td>ken</td>\n",
       "      <td>POLYGON ((34.95603942871105 1.048932790756282,...</td>\n",
       "      <td>4, trans nzoia, ken</td>\n",
       "      <td>152.595252</td>\n",
       "      <td>16.875788</td>\n",
       "      <td>POINT (34.93242693681852 1.122232330691892)</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>45259.0</td>\n",
       "      <td>very-small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>meru</td>\n",
       "      <td>kenya</td>\n",
       "      <td>ken</td>\n",
       "      <td>POLYGON ((37.91476058959967 -0.033601235598325...</td>\n",
       "      <td>5, meru, ken</td>\n",
       "      <td>61.847435</td>\n",
       "      <td>16.123956</td>\n",
       "      <td>POINT (37.86182537798336 0.00452432946090804)</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>17501.0</td>\n",
       "      <td>very-small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         city     province  country countrycode  \\\n",
       "0           0            1      mandera    kenya         ken   \n",
       "1           1  1 decembrie        ilfov  romania         rou   \n",
       "2           4      15 mayu   al qahirah    egypt         egy   \n",
       "3          10            4  trans nzoia    kenya         ken   \n",
       "4          11            5         meru    kenya         ken   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((40.89144897460943 2.86349463462841, ...   \n",
       "1  POLYGON ((26.12038612365728 44.24510955810553,...   \n",
       "2  POLYGON ((31.37738609313976 29.78891563415533,...   \n",
       "3  POLYGON ((34.95603942871105 1.048932790756282,...   \n",
       "4  POLYGON ((37.91476058959967 -0.033601235598325...   \n",
       "\n",
       "                       City         area      scale  \\\n",
       "0           1, mandera, ken  1374.518561  69.304028   \n",
       "1   1 decembrie, ilfov, rou    27.143648   8.079587   \n",
       "2  15 mayu, al qahirah, egy    81.015843  14.286717   \n",
       "3       4, trans nzoia, ken   152.595252  16.875788   \n",
       "4              5, meru, ken    61.847435  16.123956   \n",
       "\n",
       "                                        location  region        subregion  \\\n",
       "0    POINT (40.61415008564921 2.994901880312582)  Africa   Eastern Africa   \n",
       "1    POINT (26.07554312747147 44.28385681821968)  Europe   Eastern Europe   \n",
       "2    POINT (31.37155071625964 29.85801900604489)  Africa  Northern Africa   \n",
       "3    POINT (34.93242693681852 1.122232330691892)  Africa   Eastern Africa   \n",
       "4  POINT (37.86182537798336 0.00452432946090804)  Africa   Eastern Africa   \n",
       "\n",
       "   population        size  \n",
       "0     18861.0  very-small  \n",
       "1     19797.0  very-small  \n",
       "2     95957.0  very-small  \n",
       "3     45259.0  very-small  \n",
       "4     17501.0  very-small  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting files of a directory into train, valid, test splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files = glob.glob(\"/home/data/world-cities/spatial-maps/samples/*\")\n",
    "files_df = pd.DataFrame(map(lambda f: (f,) + os.path.splitext(os.path.basename(f)), \n",
    "                 files), columns=[\"filename\", \"sample\", \"ext\"])\n",
    "files_df['sample'] = files_df['sample'].apply(lambda x: x.replace(\".pickle\", \"\"))\n",
    "files_df.set_index(\"sample\", inplace=True)\n",
    "sample_names = files_df.index.unique().values\n",
    "frac_train = 0.8\n",
    "frac_valid = frac_test = 0.1\n",
    "len_splits = [int(frac_train*len(sample_names)), int((frac_train+frac_valid)*len(sample_names))]\n",
    "np.random.shuffle(sample_names)\n",
    "train_idx, valid_idx, test_idx = np.split(sample_names, len_splits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_idx.shape, valid_idx.shape, test_idx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_files = files_df.loc[train_idx]['filename'].values\n",
    "valid_files = files_df.loc[valid_idx]['filename'].values\n",
    "test_files  = files_df.loc[test_idx]['filename'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(train_files), len(valid_files), len(test_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operations on images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 4)\n",
      "(3, 3, 4)\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from scipy.ndimage.interpolation import rotate\n",
    "\n",
    "img = np.random.random((3,3,4))\n",
    "print img.shape\n",
    "\n",
    "img_rot = rotate(img, 10, reshape=False)\n",
    "print img_rot.shape\n",
    "\n",
    "# Image.fromarray(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.18694707898875562)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_rot.min(), img.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.57026747,  0.8006515 ,  0.43507692,  0.90795584],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ]],\n",
       "\n",
       "       [[ 0.52157388,  0.67915029,  0.75826115,  0.42995565],\n",
       "        [ 0.25386958,  0.56100813,  0.89804324,  0.41584279],\n",
       "        [ 0.16916106,  0.65758485,  0.45464936,  0.73364797]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        ,  0.        ],\n",
       "        [ 0.63128799,  0.74683946,  0.22950455,  0.54355905],\n",
       "        [ 0.        ,  0.        ,  0.        ,  0.        ]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_rot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.90372918,  0.29014089,  0.67227276,  0.76561582],\n",
       "        [ 0.5344783 ,  0.74241743,  0.42602505,  0.96318486],\n",
       "        [ 0.94205104,  0.8498375 ,  0.60731926,  0.53349893]],\n",
       "\n",
       "       [[ 0.48619466,  0.77127654,  0.74529025,  0.40524761],\n",
       "        [ 0.25386958,  0.56100813,  0.89804324,  0.41584279],\n",
       "        [ 0.2194654 ,  0.70626305,  0.50935676,  0.77215445]],\n",
       "\n",
       "       [[ 0.87990723,  0.89240066,  0.46209753,  0.81183285],\n",
       "        [ 0.55404482,  0.69579918,  0.18694708,  0.48252797],\n",
       "        [ 0.30199186,  0.49422445,  0.23583747,  0.41970123]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading via PIL vs skimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pil_loader(fname):\n",
    "    return np.array(Image.open(fname).convert('RGB'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('file.01', '.ext')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = os.path.basename('/root/dir/sub/file.01.ext')\n",
    "os.path.splitext(base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 3)\n",
      "(128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "imgpath = \"/home/data/world-cities/spatial-maps/splits/012/img/\"\n",
    "files = glob.glob(imgpath + \"/*.png\")\n",
    "myfile = files[10]\n",
    "\n",
    "img_pil = pil_loader(myfile)\n",
    "print img_pil.shape\n",
    "\n",
    "img_ski = imread(myfile)\n",
    "print img_ski.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(img_pil != img_ski).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 255)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_pil.min(), img_pil.max(), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 255)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_ski.min(), img_ski.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9c63bd890>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i/y6qDPJ6pZQvIH6xdyfK5rB/fjZJ0Ww6JDydtmSpSeDsAibUOk9b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\nEAgEivj/vPR+lDMFFacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9c695a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_pil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9c7c78890>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i/y6qDPJ6pZQvIH6xdyfK5rB/fjZJ0Ww6JDydtmSpSeDsAibUOk9b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\nEAgEivj/vPR+lDMFFacAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9bf143a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_ski)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.29700000e+04,   1.00000000e+01,   2.30000000e+01,\n",
       "          8.00000000e+00,   7.90000000e+01,   9.90000000e+02,\n",
       "          2.07600000e+03,   2.23200000e+03,   4.52000000e+02,\n",
       "          3.12000000e+02]),\n",
       " array([   0. ,   25.5,   51. ,   76.5,  102. ,  127.5,  153. ,  178.5,\n",
       "         204. ,  229.5,  255. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p9wd8AfhD4J3A88A/j3Y6Cy/JG4GvA5+oqpeHty21/TtJr4tm/y6ncJjRT3Sc6qrqYLs/\nBPwHg4+eLxz9uN3uD41uhifEVP0tuX1eVS9U1WtV9Vvg3/i/QwtLotckr2fwZvnlqvpGKy/J/TtZ\nr4tp/y6ncFjyP9GR5PeTvOnoMnAJ8DiDPre0YVuAu0YzwxNmqv52A1e3q1o2Ai8NHZ44JR1zTP0v\nGexfGPR6ZZIzkqwF1gEPnOz5zUeSALcDT1bV54Y2Lbn9O1Wvi2r/jvqs/cm8Mbi64QcMzvR/ctTz\nOQH9vZXBFQ2PAPuP9gicA9wDPA38F3D2qOc6jx6/wuDj9v8wOO66dar+GFzFcmvb348BG0Y9/wXo\n9Uutl0cZvGGcPzT+k63Xp4DLRj3/OfT7PgaHjB4FHm63y5fi/j1Or4tm//oNaUlSZzkdVpIkzZDh\nIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnq/C+9TQ4jCayuZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa802b4d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(img_pil.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.29700000e+04,   1.00000000e+01,   2.30000000e+01,\n",
       "          8.00000000e+00,   7.90000000e+01,   9.90000000e+02,\n",
       "          2.07600000e+03,   2.23200000e+03,   4.52000000e+02,\n",
       "          3.12000000e+02]),\n",
       " array([   0. ,   25.5,   51. ,   76.5,  102. ,  127.5,  153. ,  178.5,\n",
       "         204. ,  229.5,  255. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p9wd8AfhD4J3A88A/j3Y6Cy/JG4GvA5+oqpeHty21/TtJr4tm/y6ncJjRT3Sc6qrqYLs/\nBPwHg4+eLxz9uN3uD41uhifEVP0tuX1eVS9U1WtV9Vvg3/i/QwtLotckr2fwZvnlqvpGKy/J/TtZ\nr4tp/y6ncFjyP9GR5PeTvOnoMnAJ8DiDPre0YVuAu0YzwxNmqv52A1e3q1o2Ai8NHZ44JR1zTP0v\nGexfGPR6ZZIzkqwF1gEPnOz5zUeSALcDT1bV54Y2Lbn9O1Wvi2r/jvqs/cm8Mbi64QcMzvR/ctTz\nOQH9vZXBFQ2PAPuP9gicA9wDPA38F3D2qOc6jx6/wuDj9v8wOO66dar+GFzFcmvb348BG0Y9/wXo\n9Uutl0cZvGGcPzT+k63Xp4DLRj3/OfT7PgaHjB4FHm63y5fi/j1Or4tm//oNaUlSZzkdVpIkzZDh\nIEnqGA6SpI7hIEnqGA6SpI7hIEnqGA6SpI7hIEnq/C+9TQ4jCayuZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa85ab0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(img_ski.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188, 188, 2943)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(img_pil.flatten())), len(np.unique(img_ski.flatten())), len(np.unique(img_tif[...,2].flatten()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Transformations on images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/home/nbserver/BEGAN-pytorch/\")\n",
    "from datafolder import basic_preprocess, default_loader, \\\n",
    "    grayscale_loader, flip_ndimage, rotate_ndimage, adjust_range_ndimage\n",
    "from utils import save_image_channels\n",
    "\n",
    "from skimage.io import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "myfile_tif = \"/home/data/world-cities/spatial-maps/splits/train/\" + \\\n",
    "                os.path.basename(myfile).replace(\".png\", \".tif\")\n",
    "\n",
    "img_tif = imread(myfile_tif)[...,:3]\n",
    "print img_tif.shape\n",
    "\n",
    "img1 = basic_preprocess(img_tif, 128, normalize=True, log=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ffa677a6b50>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R4iQf9UoM74U/3OSW98go3/k85KQSUtbL4J39mmv/j4x0z/y9dyCtuHyDXizVJfj10BG1\nh9iMV1aXMA2qr0g6O3UQO0U1Orms6np+Km0eT4ErX7Bfn4PqqblTMMYcDHwLJ9NZYJ219kpjzGLg\ne7haYo8Cb7bWbi97sCM2wxcuqM+u6/splD2OFBrtlxJ1ty4lksP+5ld1XECr0Tf9JgWIF2J+jOLO\n4DJpFwD/VN1hj3mLa/Wl6SL2XVCd2PAXiYLRDhYPyGc0MGqC2IflDO/Aqtn/grQ5yk77ki/tUmID\nUyMSmusxtKNZTfwi1xPWn6zA2kVhZ6DnjLJbf4R6qWf6MAl80Fp7NHA88F5jzNG4X+Z2a+1K4Hba\ne4IeCPzJUbOkYK3dAmyR5V3GmHtx84A3Aa+Vzb6Jq5xSYff1DSJNzaCk7Ko0/NlXNJbtmdX0JjUe\nzAIYl+79B99z7f+s8Jwtx/uBL8rys+U2LMYmXQ99XMSi/f1+mJfUeGk6kvk5F7tFmZhfSGTg36Zp\nx2S8Gp5KEb+vJI6iVMVamR/NH231a+6gsOBMvUQ1HqTdSLH0UG0RG1/69aOfk9dtqzzuNDREp2CM\nOQwXZ3MHcIB0GABbiVXGyX3ORFXj+6VtEQgEZoO6OwVjzFzgR8C51trnjYm7XWutLaz+hP/ZOpzz\nPWblCkvXPwMvgXtkTvn0j2XLzdKOUbEzSNm5m8ydN6yU/5dAXhxl2k5CSKZcW0elyTljRKloNUtz\nmqTgzFtmT9LbpwpUmWeI811skEmxzYE5zC2/XLzvNko+i4m0bNPnAJ+Q5YOKP04mgvFKz22Tx/OA\nnRRKL41CH/8poFMqYXVqCrtPpigfP0GkT9H0dL6EoJKNTvRzxNdd0ju0PurqFIwxnbgO4bvWWn2L\ntxljlllrtxhjluHSH5fHboaJ84FXwUL5hbaIr+yQXmJP+Ze9ooCoL8OQKBUn3Z2dnMjR0V9hwE1L\ncSXxU7FO2oXEJdkkkCstP2Eq+vQtodg9V2z7x1dq0dDrGSXyhdBpoP87rVKF5ufgzoXePkBmuic+\n6c16DQxJfkIjj1y3hBNbopf1AO2cpog7qtQp6melTckgXo7IkrEWJLkJGfGDyQJTksF60GVG2kOW\nPqO915WyvXihZj9a2MlUdN31U7Oi0TiR4GvAvdbaL3ofDQOaWfN04Ce1X14gEJhp6pEU/gp4G/B7\nY4xUFuUC4HPA940x78TJnW+e9kgPIVLAfxDVDDhBEnxoeGs1tuQiRaMotAbnEYtqbkTq6H+S2O++\nHdAR8hyiwq9+6rVBkRB0FFEFVZ5iTzggGheWiglzTi88qmHXyRJolcbgqhIwJR9aqrT30fhJTJrg\nKmQTGVZkJS9hVtRYeswp4kv3C7mUnWbqhmryTH9G/k3ayKtCjzm4Nt5IryML0fOddWHsffZwyIsn\nrfmjtFLItuNSmBJpTac4drrrrp96rA+/pLTe9oQS6wOBQIvTgqXohUqr/CRTU3UBe9VB6UhZqd1s\nH3tucYqpvh4ZOv7aopGCsMdr20F6uNE1G2XOOrkDTpHCrFEMiMzvc+OQFykiZQDfuvwHABw4dwL+\nIPfh1GqVllIMFS22+jxxZa33F2+ehjoZabmICeJRfroUZyNy/h4Rhaw4PflV6ZSSJjxx1Lr7la49\nVhXdW0gmYx1nmmokSamn+2owIoX1HO/aQw6OdbuPqKSgUY17YVyjXOUmrHmOWMlaNaEUfSAQqJ4W\niX1IIamtHiKeC+uI4adzL4iOlNRe68UBokf6vvE+svNFQx3VHdwNA5oZQ4enLHFSklau9yCRgvPF\n4vB8F2yU5XFNIyfz+8xovFuB1dE5cR34qIx53aOQT9aHqBSNNlW9zR6mreyVJJqQyly+Kwfj4hSr\nKdZPLbVzwnzpf8+KnHs+jTrNjB/kHrbu6KHrLdp62m+WdGga3wFGdAoHOmns7mV5XjIhKdgfl4dy\nrz6Pi4mk1zXqjKb6mubRup2CkjZt0CnDRGIZXIfxM5EVO8TW3SM/9LzFPGsWuOVDXUfRfViG4nLC\n47R2Z5BgqzxMA2l5LsXB/w9TcEzazvLQj2jW5U7igiLVIj4g0TQsT2VVrT2sdmpHuLZ7N3TKtCDV\nZ6ECKs6svBQtrdc9piK89ix1PA9R5zAvLpiz3T2wx07eC+NSdGdcFY5yztVdFKem1mc1iSaBqT85\nUJg+BAKBAlpfUkgjzWnDd4qZkkg+DccVAaDbLGHvctcPdi2WHntXB8VlfropNse1MAPlRjFx4Ck5\nI9DvrB6N28C8t8S211J+5K9xJPdZLdOekf/l2tx86BTJw4r35fDZ5acDRWrrq4kf9bRwcDU7biEq\nUtsUf7Y89Mt3+alIcM/ugb0ytcvpNap0sotiA19awPEVuAhWiHNnarq8LxBLcGsrusogKQQCgQLa\nU1JIQ0eRwU4Y14zDEnE3KT1xx256esQ8qQOkfZbiKqFQGJbW6mi6slIjPM7VrIivAYfIsigHx5ck\nCsT6NEASmBZxpT5EFMK9GXhMdBxd8jtNXgqD4tST5j5dxBzK50fT3BP7EzmHvVTNfioxjlOzmVqv\nbQ1Ez9VeGb1/txemJCHKgOoD0pLEfl7aC7xlrZR1bnzdRebKBaiepFL2nU5hIMpxTWQvH5YfQDMr\ndT7P7kmnxZ03Juv2PkCszFF/hikq9WhrDcrnsClGK03PQVO8T77MeQF2/L9xGNe5hiY60XuxhTps\n5BXiOubfy5Thdx0TvG1Kfp8oY/KH43d8UPwwskBW/CqiACctWLMfsQgtIfMFyWJkysC1xN9P4z/U\n5yJ9/qV5YnrSPkxmXeYDOIdfYI2K+/7gU65kolo/riFWNvrZp0r9LjtKrC9NmD4EAoECWtejsWau\nJi6ZJocdEuVO/3Ie/KUbEY/Mi6fYrofhFDWhqXh8HXG9hApLircVGu04FwZfCoA9wZltze8fgu2u\nKC2nqsLLDxioMh1brdwpYns+C9tEOegPqrlvuna5+KR0TcBjd7nlVctdu0EE4YklXkZjzdg9Sfxd\ndPr1NJUq46ZlkFgOTzWJ6hSgl+pLE4rHLh+r9qqCR2MgEKiefUenEOH3utKjZtQZZ4ovvFp8yDXB\nxykTFCvQ6sxiPKtoFLtOus9J2car69Zxn1vcK/P23c9I8laIJ+cafFBlird6eLlnCtZ4iKw4Nk1N\nQadc4xLRFXTn4c/WumX1bu38lmv7Dof/kkd9qzgKDWzD5R326cFlxPbRkmtLqOq5mPbN+pfpNihD\n1RJCVeyDnYKP3LzI1HsD16lwtErdfvPEopy+BDOYar7haAe4pcw28v0GPw8Z9wh8+DFJTJJ53ktw\nolaZagOjfFSpWUNVcUXlWSMdfsdXiWpa6tf0jQvRNEN2PHgU5oo1Y4cUpxmeC2selu30+2YpDtxS\n3+o+XMpRgCekTXk5dXMDnJL+derhAS5lZUoGMr2STxd9Uj1h+hAIBArYxyWFBLdsBiOjwkmqSKww\npLdtUPNaBanTBhbBzW5cuOyV4sK3YYJYtNpfWjVNvr2G60lLq5ZIGVcpajk++V2ueADEYRZdwB1i\ngsxJqJKRx3vnXhiXdVNisz90LrGZ0Tfpiskwqkyg0lIXsRSmuYg/hebJnBIFZfZUMWEO9hAXlmhc\nseKVzCXNJ6EREoISJIVAIFBAm0sKNxIn8aigTNZ4F3TpfLCco8i+wFnTb8LTYNV8J840+VGY1BDo\no6XVRCM3EJv01Io83WifFjvyYmlvk/Ze0hWiQlLYGKQ4A3MX0C1OPZ0iDeSk7XkRdDnnH/tX7pE3\nY8CQjPj9+ho8jTpzxY5Pj0j7JIy4e/RdMTG+lWWM8B0AsgURtoinZSMKSiRJUXYmq19BFZGhxQRJ\nIRAIFNCmkoLOqRYSu38m/cGJe9AOSZhiF8N4dWW592mGLiBK19Ypo+w4YN0IZ8WHyaxQ1X4ncZyA\nSgqfpeo06JHLsT5+ZaQEKM6pMQJMJNzPe4C8XFNW60m4kd2u6ICMOCKLZcJ2TGB63fk3bnDf6cRV\nOWI9g3znYU0YMwGTTi/x1iiKsYelUaoVNYOUiT+pGEn1Pt19URrsf9imnYIqWK4kFna8dM/JWgcd\nIhrbHlizhD9Nrie+V/LS9Fu4RcyNkaR7Lap4M936oCfrn/lU2yFAzQUL9HedgqJYlNz1MC7fb448\nC9b1AKbHYjtEvJcO78fZPPS4qdCJVoq13LoIJmVeokF0WTlWPgsDvhkb4DT+soHJTWIq7AyUBpaM\ngzB9CAQCCVpMUriMWDytxHusRI+q30pHv7yMEqvGSB/t9iU0t6SKvV4cyEYxqRkZ8d64K051piJ3\nxvPu3K9cKG8aagbVe5xm7r3Cq/ql1/g82ArCsjU4sGAoEw/EiSzME4lmSn74Z933tWMT/M85zgPy\nB7Lz34zthQ6dxsgBe7ZDTkzVO1WykGOuzpCuOGykhNAaBEkhEAgU0IgCs1ngTmCztfZUY8zhwE24\nIeou4G3W2gqH5w8RO4/UiG+eUXfXcZk3j6yD1eWSbbQ7a4BfybI63/xM2uOwS1wm4N9nDwbg2F/s\nhA5xWuqQETJ/PWQlBn+9HKNk9uQku8t8dolrhhZAp9bj0JwCfwR7eWKdH28hJlKVJjrxhjPNbjwn\nrrS0V/QCc50p1WzJwiInHRnVETw8BRlVGIqy2uyJj3uqnl+flwniNGmaxGcmKJU8pXnUHTptjPkA\n8HJgvnQK3wd+bK29yRhzPfDf1mpapBLHONJYLsNJZ/0Sxjqkot1H4w2LMjtfAnzELWoaba/CcFE8\nD9Rlv21d0vJJim/Bf8vvu20HHOqCe+zB7sWb2Jane7O4BKoSPX8f7NKcgfIS6n2cpPaipiM3uLbv\nUNjPTWn2Hu5+mN5fbYW8BCrpHOGN8vLetjtepxdizvb8FOR56ZwDPRIktVPEfFEqMi8DC2SHLrFC\nPHwAdKkno9tva5flwB0yTvarv4JOGXYSJyxJZqRqG5ofOm2MWYEL+/iq/G+A1wE/lE2+SdMr3wUC\ngUZS7/ThCpxjgLrALQF2WGu1H98ELJ/2KBnifFbrRVzL6iElfdb4GAxqz67KnSPAiJ1qjUT5bTSQ\n2eaW8x8vPE/SC26fQUOh16ERjfYZ8ebrdfczvx9knxIRetfj7rO9eyEjYtWU1sogVjYm68lWPfP6\nEgxKlui+F7h2wX4w5X7b3t1q4suAlfEp/6hrhyUbcanCwlHBWM8vQKV7k9jmf3j7bZD21G/AsObr\ndMlWDswC/Vr6Tv0VNL9hN7FpVr0dNxOL9cnUdfVEljaam6R9S0Vb11OK/lTgKWvtXTXuf6Yx5k5j\nzJ2l048HAoGZpt5S9GuMMatw4/x8nDfRQmNMh0gLK4gd5wuw1q5D3OnMkcYyjps6TorPvo7qvZ9y\nbacllhAkJiyb40apV3DaRlew8/aluzhBz6gjRqRHuIbGeJy1GnpfbohXzZFhssPdhIn5HTAqEXz3\nSS9sM3Jfgd2STGT+ZDwgJtVNHcTJSMtNCiNlbxdkV7rFQ53AaBf3wIRILDslaYvZGXsjToqEoL/d\npHcdOvL7Q1lU+p1Y36FOTpH+41tEyVJWqd7l7bBGayQI64ENKiWJxHCqnnw7sU5BTanziElLnqtK\nQpGS2MXMZMSuj5olBWvt+dbaFdbaw3Byyc+ttW8FfgH8rWx2OvCTuq8yEAjMGM1wXvoIcJMx5tPA\nb3HFBcrzEK7HH6J4BLpF9AK+38iUTDQ7HuQ0MWHZlc6sZKaeh5fJ1xrWNN1SaJaltEfh2GrR7xQr\nTX6ZdzqWl066fn9eJge98nlGRr/8PB7KOEvAEbl73LqJLfHInPTVyRA/MWrtWQMMX1e4Q0akvayF\njJg6e52G33QBqKVBdBtMwJictMi6cTXpkZiJnAz+fkXHeJr0R/3Cwn9PJQ7czMqIPiym0jXnpezv\nk0zw+3HgIACs3BfT6CCFirmnqq1bN5uzioBpTmTacYyA5tnLvfJwADpHH4XD1Zypoprm3TsEeEyW\nq/QvbwuuIvIivE/k/HlaM68D9ogm7il1G+mEvGRDHpP7MjkJVvbJOa9SyeLIi/xTqSh/CnCzKNky\nago8Q85JXKTnKPEF6DGQk+u4V44xZuEUfQS0Y5uFcn3DVxI9F5pPRZ+/E6o92FeJe1fNBDPrA1HI\n5hwIBKqWmcTdAAAgAElEQVSnxWIfPCp2knkAgM5NMvo8+TSx772ai57z2rUNuLhWxYs1eJEkB1kv\nyrDOhXCi3JdfqO1uL060BiblUTDPwbiMcGImflEkx2kSVmDcT8Qqmt0usVn6ispxiWG5W5TDHR8j\niqUYFwXpmjit2ayy5hwi5eDN8uyoU9ftVCktPElj4iI0O/fM1R8JkkIgECigdXUK1fJzafewj7oy\n18s6b1mkgpFRorl7Wop0nU+rYLHq0nibnxWnGS/aHopTMfjnaXAegIaguqzkPegGrNSJeP1DsvJi\nmo/EjKg7f31UpFNo705hkNgT8mRpRwidQgpbgQOj/1Tx6k03ImvCVXCrrNeQA60N43sX6gt9a8rJ\nphKtv33L8EXiL6h0EV203g+1QmR/DJ1Omc0esZqs2USb+b0ERWMgEKie1lU0VkIHxd9gWilBReAU\n8Xcf5sCC/1KSn0Qj+XPxCN+ZaE8hHkGVckHxLSMdXA6U8zNQ+2ovkeJ1jZgRo+/7BBzo/A44Qbfv\nIDU3aJsTJIVAIFBAe0sKvslnRMxcZSWFq4jz+ktMRSuYwlqKtcWrfOVgMnKyZaSBcnSlrKvQxKff\nd+ocyGsdJh1Lu9mXJASlTTuFtdIeBBwly0dIu5nSBWIXE4fCLm3Kle2T6ItxM00pmupopvv52PSb\nRFwmreQK7fdyhQ5JGdeb5f+m3YvZJUwfAoFAAW0qKWhftoLtWReSu2g/dVbPEacn0xgIrU0wgcv5\nB1EobWB6ImXi5yhIj9cwvkRzSqwpfcQlWFPKxxfwIdcMScCVxtmsJ647VLaekEoaH6rqCluJICkE\nAoEC2lRSUJvZfSyaL933Mi0tn6N0Ha13EJskG+Ihtu+RrK5VwBJYnyjXlqP2ZK4Rz1FDrrcqqKTY\nLoXfvVNCstfL/6fi4h8g1luuB07V+BotT590iKoEVZjrc/upUhvOCEFSCAQCBbS3m/MIMF9MivOl\n+z4umBjrRrXr6kIe/TpXEA2TrxdFw9C57Z+vWyWETkrXuLiNYmHGXgV7l7nl1VJ/4hbJRTH2OCCR\npGUlqc8SW8TSksk0lD+B2AeoLGdgoDq0U0iT6DvFdKg63Oy57R9rojklMxTLzuazsuAV0u2QxDE9\nXZB/oVt+jez479IpvHYUhp90y2tUM7mbOHtL0zuANELsQyAQqJ42VTR67MtV4GYLLV2Q9F7MAJ3i\nXKTmuUnikbbdJAaVMvW6B4mTqkTPlc6h1kJesjhnZaPMUuiSMnd3yPRhQm/aHsgvlmXNOzdFFFvR\nwgRJIRAIFNDekoKfT6FdR6tWJKmf0RE1Qyw96JPja4P8DM/tQHJI9BWCqlfJS3RlD2DFHJvVBLVL\n+LcD9gfgtDniFr1Xalr81y541kuQCzgnqpQI1RajvTuFAeCnslyqvFigfnyvPn3OVczuJJ5uqFNi\nOyh/h4inCLdIezJxZ2AlN+JqeUVufT9MSo84KQ9bXxenLZSeRYrbmo5N7v95j8AOVTBqhur28KIN\n04dAIFBAe0sKgKtUhzcq7Yv1HFqESWJJWAfBHoqnbDraphX3aRV8x0P1ULydOKByUkOrJST/JGB4\nt1veLVOFQx/EdDvx6Od5GV8f3uLaPWNwsopVOuearqBMaxAkhUAgUEBdkoIxZiGuFM4xOJXTO4D7\nge8BhwGPAm+21qZV36yfYYj6tWGZ0LaLkqsd0dJ+UCwxpNGKcuiIt9yd+Cz1u3iJfNYksjf/b8C6\nQrSv0++6XaSC3ZZim+4VRHkaIpFkVpyYylKXR6Mx5pvA/7bWftUY04Vz17oAeM5a+zljzEeBRdba\nstFHdXk0FlUYjtGeaFHNBw+UxO8cVMmrL5nKnxPAKS2SwzDZmXURV3Oz3jr9LpUMLkPE/hrzpdUy\nmSVdm78urSofNzMzqeKBZns0GmMWAK9BCshaayestTuANwHflM2+SevOKgOBQAo1SwrGmJfiKozc\nA/w5cBdOy7fZWrtQtjHAdv2/zLFmPwAjUDuqWFQROifxAp19cOJyWamefBWGMTeSEeLhr8Nrx6VA\nzioRD0bOrt7PRacjelyVNEoFVs0uTY996AD+ArjOWnscThgrSMtjXY+T+sIbY840xtxpjLmzjmsI\nBAINph5V0CZgk7X2Dvn/h7hOYZsxZpm1dosxZhnwVNrO1tp1SC2z+iSFz0grPujtVbFn30D1aTpN\nPlUm2rd0ws1S4n5KFGsNVwRrkpMy8/KuS4nqfPgjelauaUTcYmvxhtV9VGfRowlSFhGV5CuLH+o/\nu8lVlJolBWvtVuAJY4ymUz4BN5UYBk6XdacDP6nrCgOBwIxSr/XhpTiTZBfwMPB2XEfzfeAQ4DGc\nSfK5kgehFklBHEpYDMOi8p4r/VtuG5xYKsV7oKkkq0etIZ5zW29dKp+TNpkY9mriAJd/8tZrWj1J\n1T+8m8i8p+7LmoL9doonsWN48/6vSTsBnE1NRJKCtCdVuuN3iEWsM2o7d+VUpFOoy5Jsrf0dkHaS\nE+o57vQc6pqNR8AS0WH2iAz7zCPA9bKdeKBFBugLm3tZf+qkvfCrE1mU1xO/AwV2qVJZot9H+tRA\npgNDMn3MzolffI3BuF3iF2wWxhOerjn/n3eWOHe9aGej86u5gOYSfYe0m5t07toJHo2BQKCAVvQ5\nqwAZavbPMbXceaNketzwYCbnAzsS22sO/ssBSZ8V6T+3UpBqK1A7RY5knyb26hFOJTZhlnE8K+QT\nZT7zpoo6HfiZrpDnZKIPkLDn3AcrPGedDAIDaolXs+wOYpvlJdK2XlbxICkEAoEC2lRSeNw1z4yR\nXexGAztPfcq3EusQktqlvfDfkjn3z9U/NQvI3LPSoqOBdJKj7/DH4vm9b+5TBeB6qkQrf00S1Zz0\nn+CN0uYulwWJSlxV7XkawMDnUTO53ewyPputC+BlGvOgJvSraLXEK+2fzfk317h2hYhq922F13yw\n9Pa/+oprj9cfZRx4W82nD5RhmNgSoIq9NKf3Ear3EUjGMnR659L3rpo4hnrR6/E7qVPl2Xzqr1y7\nx8Lh98iHGniRozgppg5OV9PggKmQzTkQCFRP+0sKyu1aj+Bc6P9M4sPgtzBr+DUVwLkC6Ohuvc+S\nUcZKKQkiKSn0EJs6tS0nnTSbYWIJ5bHvuHYCWKkFjlWk2EVsjlWFqoo8hwHPeNv529REkBQCgUD1\ntKmiMYUTRPF0C8BBsjL0ebNOFBsg8+uu9xZGKoIb0ZP1O1QfkPSSLIdKCCqBzGbQvh8XfOg/VLhT\n0knrC95yKVGq8YS3JhAIFND+kkKy1sDJwPAZsi4Um20Z+sVMPHIpnChD+k/Facx/CtWEmawvkUQ/\nj7RRnyFK/TRZxvo0U5TNIFIps/M99gFF479KK3ENtwFvrPeKAs3jcuIcZpJw5Wbv4x4qIxldczsw\nJoNATkKQ99mcX9dK69eT+FglOwZFYyAQqJ72nz4kIx9zwK2yrE4s++yI0Y6cR6RAU3Nljti0OFG8\nR2Ra7Ez5TJmAVklSAsAGyme6rjn24nPEhSo0ruQI4lDyD9d64IggKQQCgQL2AUkhwSnE0Xf73rdr\nQa4iHt4/VG5Dx62AEW3i5Pdcu/rv4s/TErXo71nOKufnR5gNyVCvURWlGW/Zv56hxPadVJjkVSWB\no4hFJnVo2l28eR3sm69NUjRrh4KnbUuWqgTOTmByV2LlJURvdRSnoG/5xZWJ2rNdBEg7AH2jxkh/\n3nTdkLeuoorpeoIsMCrL6u34noovsxLC9CEQCBSwD5gkE/gRd9ob69GbnVgjUMggsU5MRf85xApg\nNSfnLoR+qcHwK/FGPV4T5TzADFZQqp1kLMYppTYU/OlGpfsAbrqmN7DqqlvBJBkIBKpn39MpWOI5\nmjrCvKHcDmkx65fQimmy2hNJotsrw+HUdsiIQjIv5uR+iJRmc1VpqY/mMu9Y9dSlVIcf1VWcU2rD\n+pjylsulmxvwtsmlfF6S5idk2femD1DcKfiJmMoqczRjz2HATlnWUNcWcJ1tG7RS8zK42dnShzrc\n/KH/xIfhdgn/VSn4FGBQX3jpFIx7u37bP8VxUUr/FkanD+qsuZfKFdsV56qshLXS+rkxo4xiYfoQ\nCASqZ9+bPqShyq5pYyL0dnQBR8vyNmk1P2AlpcD+1NF8mUsg5ySt/kNTiqxo6foRwMiUQG32MvIe\n14CreQBY2YDjVISZfpMi/KmE/39V6PRIM0frPCYPXCfLlRW6CZJCIBAooC5JwRhzHvAu3Gz997iy\nccuAm4AluPL0b7PWpnm0Nx89a1fZrTy0XPoh5GRO1hl1/XdLew1xdFqTlFVtj9of+6BfKiL9p6wa\npjiWoZt4YNOIyaqUb+WZMSkB6suFUrNO4UtEku2vj3TtK3S8f5TiOijlqVlSMMYsx6lCX26tPQbn\navUWnOr+cmvtkcB2mleTKxAINIF6dQodQK8xJodzS9kCvA74e/n8mzh16HWpezeFi2EqmdxS6jqs\nn4RTy5mzVMt9BZ1RfjD1K1dx41ncVw2URitufZuodqJkOWeYWCpQIWwK2E+W1dgTcFSkZzDwoDO1\nbV/qxvmFTztp1uyXp9rXvOZOwVq72RhzGa4yy15cepO7gB3WWpWvNxFrPmYIA/1iGx+Rwi9TIkB2\nPwsjV8k6KYTdvzblGOd6y+pNV1cW3T9RHqNInvYrUev0bjXwf2V5jNK0YgyLXlMyx2SjKNcZaPDY\n1DbIunoSi8yTslLjS/y6EpVRz/RhEfAm4HBcptQ+qijAbYw50xhzpzHmzlqvIRAINJ56pg+vBx6x\n1j4NYIz5MU5IXGiM6RBpYQUlam1ba9cB62TfxjkvDfrO5E7psueIwwDoYwn8cbt8pp5NVxNX60kr\nhx4khNopkSIszYFMn5K0kVGT5uiAlxbf0g9x6Xf9PWewHJs+cs1KujzoLScjMg/8JIjgS04S2Lyw\ndme7ekySjwPHG2PmGGMMLmvePcAvgL+VbU4HflLHOQKBwAxTj07hDmPMD4Hf4Gx0v8WN/DcDNxlj\nPi3rvlb6KE2gw4BRX1M3aZ2z2JtTdUmXPqaKQ0N8Gz4rbShNP+MkJQQ/ilDlyHJP69AVwFK33N9A\ne+Z02EQ7kfi/gI/jhGeI5/wVJKaBwvuj90bd9xfgcq8AjXDHb5HYh0MsfBCGzoV+NVSo1n+6nHOS\nl2/ES+feLYVHWOyale5h2TlnIQvufsKtGxN5a3U38S9Zznqq/vxt4Ie/LzFUYn1a1ucxUSJbscsP\nXJSyUZOZVhlaT1BXCW6lTD7StcTxECH2IRAI1EBLxD4ccWSGL1w1h/4MsEFCZcdFWVTOJDME9Itm\np0uLyl4A5r1u8Q0iRdxxGgALeq+BN6hkpMbyccrbwa6v8FsEmkLaiDtEcdbnLqBbFIvlsig3kkHi\n51M9MadNlNJACUFJtfmtlXZx1YcLkkIgECigNXQKKw+2XPEBYApOnSdrRVk0IglQVhM7veglG+Ju\nrcvbrdRI0UHsZOIrhlKlkSulDfENM4PWbKiy1J/O4X0dw0zV+xjyzqFm04o9dWaFinQKLTF9wG6C\n3AfE/prwINSXfshbnvLazpR1paYcI4CRvICdkvUn1a58LXFc72XSVqglDtTARcAiWb5C2nNLbFuG\nkp3BRTRNQTwkg8eUDB6RovFTgA5wNXyXWSRMHwKBQAGtISk8hJdoIuFB6CuU0pQ4gynrSjEFsL9b\nzkrqtcnzUjZ8D/GINZXyeaCxdBHXMqhxOpsnRUL4smtGpuKfsaFTim9Dj1xvkZTycVyJt/YjSAqB\nQKCA1pAUfKpNNFHN9v3AkCRSsRdMs397zQPbm4rKqKfjK4xvTnyWkShZ09Ukge8pyr9CabE0rU+Q\nFAKBQAGtYZI80li+wMzGyftp4MvWhQi0Bb4jkTJ0iWt75sCYKKf6G5iqf5A4pXtSp5B2PbNPRSbJ\n1uoUoAEdw0XETgtlxFK/U1CzpPo3VF2s9BpiMfLd1e4cqAvJqhXXNoiJfBi+xPiYy/XT3a95OM9s\n9oW1IiH2IRAIVE9rKBozOF+hhvisT+ekcqm0En2ZI3aAinL2f5zYw64ca12zfjnYQ91ylEDkeuCs\nCo4RmB79zVSq9eMHNF/mNcB7S+w/TmeBG2y7o2Z7/S5rG3r0ICkEAoECWkNSgBnsnkRCUBNVFpiQ\n6k+rRWt0q4UJcTxZU86sJGXTuxeT/7MDAcjc6xJo8uLfAF+X7VQEUm1UmsNUoDQqDaTpv/Se7i2z\n/wfJRO7zWrD2M8AFDbi2EgxT7ELfMEX6xdNvUget0ylYZsj6IEku+kUEXf8ZmHyRW77jANfufcRL\ngKuh01pQw+skVJF10Cjf63Ev/lif83fvYX+cHRtibz1VgDbRF3+f47Nwp8RFvFxf/C8Rl++r1J9E\nRW5N4tNXasP68LM7q3Rftqhx6xGmD4FAoIDWkBQscSW2qnP7a15FjbKbTrmXSHKR64CsE0vtIjd6\nmM6DYZdmBFbxVJNVXA3rJZy7V8xaPV/lNJkh3DP/YLdwWy+8UecommpX4i40l2CgAjpgjsrh+pvU\nkDJZn6ehswv/bzQ6w8k18RxNJkgKgUCggNaQFB4kLkFeNZp5ucrkHDq3nJeB1zubpHnMFZWlZy6M\nSeLY2yXR6wnaf2ZhoSyqdPMgML4TgKOPFalgv0lAq/XoDvp/0CdUzoej2qkNpVkeh3rMUgln24DW\n6BR8VPyqqIaeTyV+BQCfllYUTWO9qCnCdstbvrADMypJVrbKLfqZ7NaRgVdLva6bRZw95VkiZeIv\nJE/gzkqvO9B0xoBbZFmVfzORIUnPqTPQ1nN7TiVMHwKBQAGtJyk0vTdV5Z/YvnOLYYMrJWf2PODW\ndS+AUZkGGJEeNEZkxzwYEqVjXhVeWSIT2V8367oDVRMpF4GTZVlzKa4nnrIOip/KwDYqlzgrwCTa\n1gySKiJICoFAoIBpJQVjzNdxfepT1tpjZN1i4HvAYcCjwJuttdulpuSVwCrcJPsMa+1vmnPp9SKS\nwMAkkanr1yIVmAnYJZ/rfHDycNf2Z2FI9AeTWvrrbO+4qvA8AjijwdccqAnfNKjK4Q7ipCy5f6ap\naGzNDFazq4dKJIUbKFbLfBS43Vq7Erid2M3vZGCl/J1J7D4WCATahIryKRhjDgPWe5LC/cBrrbVb\njDHLgH+31h5ljPmyLN+Y3G6a4xdfRNXWh1r5IlEs/pA4Qs07HPaIFLBG/e6PkTYDPCzLz0ibVoPy\nGmIT5L827GoDDcIvGaDtJI11OFJJRGuNjDX4+NXT1LoPB3gv+lZAggZYDjzhbbdJ1pXtFFKZMYWM\nl5yjX30ersVldIY49kH7rSyxsjJZu8ynVBhvoCXwX84Rb13VHrVl0OlCm0Vr1219sNba1JF+Gowx\nZ/Inmv4mEGhlau0UthljlnnTBw0H3Awc7G23QtYVYa1dB6yD5PRBTUILpFXZa5Kml3DTKYvJQ7/U\nfRja6tr+++XDLuBZWdah4MrmX1ugsfjmQT+KUacS4p8WhVnUIzmocrNNYiFqNUkOA6fL8unAT7z1\n/2gcxwM7p9MnBAKB1qISk+SNwGuBpcaYTbjA9M8B3zfGvBN4DHizbL4BZ458EGeSfHtVV7MB4ghC\nyT0wLuqKgS1EuRAaXs5bpJMBMScO9cCtIqGMhfoP+yQDeJKhtD0p242lrKsEP/ahq+RWLcm0nYK1\n9rQSH52Qsq2lHg3bKtwLCXDMawC4Z67T/h+96RFYcZ9seK20o1RW+FUto2eX+Dyb+H8LReHNM2YN\nCcwYyeAlX2+sL3I3tdFPHPvQZgSPxkAgUEDrxT7oFWWc7vGwLhnFRydhWLptrctwWxbeKH7rpHml\n6XRjibSXEuVoLMCdYzvfAGARbyeqJ6AjxqoqvkOgvahIAfhpwOXhZPBdri0nNY4QT0vaxJNRCZJC\nIBAooPUkhazoJked09CcblE0bt8Oveo0JO1kHjZKMpTU6k6qkNSS4KUyM38EgEVc4/7t/zoMiohg\nLinYJrAvc6W3nDAxD30MrDxH5ZyRVD/RSWyKbEpx2+YRJIVAIFBAa9SSVOelYVIiyiRT0pqPwUZZ\npcaC1wM/lWV1MjlxurN9W9rnpfWNJWulXUFcOVQYesS1/RPELs8NjL0PzAJJE/elxGKAWLX8kV+f\nO302y6VuHyGWEJrltBSZVP8VXnSEW94r4slxD8mHn/D3aGrsQ3PIEr/ckZXQKxKrL/x/SruBuGL0\nMBWiwlGaDKif5dBbc5cUD3lJv/u/q2zREYAvS9tBeqBUoHVI+rukKKH9RC36eOjANUzpYsSraV6e\nRn3W9e1d2gnzpDBRVnsidTK+CJgry5X594TpQyAQKKC1JIUJYjNPOWch0S3Sh3hBUoXJ8K1lPtM+\ncikMukQqL+uVylAniWfLoIUBtVOKYrJgCqKl6K8nsA9hiZXZ6vmY6qnoKbWbMTMfpPit3f9f4CmZ\nFu9SCdiL/q2yAG2QFAKBQAGtJSlU6kKs0/o0pWJDkmPugQFJsnKrxH1t+I5rV2WJ+9Kke7TPWcT1\nHbT3TqtNcbW076vtUgMzwwCxjkDjIQpiJUQyvEXEg5NpjEv8YOL/AeLks8qTwK63ueUTvykrtbjx\nY1QrKbRWp+BT7ob6Ytl6aTUzr6GOOAVVat5AJBuedLmsWyFtF3HOmHIG64uBQ2S5XKBo6AzaBl/p\nCJD7LKzXEH95FkwDMqr4HYEeLm2qokkSfUtHlO1LyxxWX6IwTB8CgUABreGncKSxXEZt4pZKCr4p\nU+3IVduHVTl4FnElKckZM/wCALavWciiKOOcFo6d8i5AT95FPL0Q0U5iK9z270qc+2qC1NBmDAEd\n8px0fKzws3ryMfq+EYqf51Hl+4JSi1+RVkuTqTZ+Cs+fpiI/hSApBAKBAlpDp/AQtStl1L9ce9Us\ncEq1B1GpQCdwXyO6NUNy4C6X12HyycWwW+o+vFDMlYxRbKfqAA6VZfU2ecrb5jJZ1v3mVXvRgdmm\nHxcTAfFzGA2z64i9ZivJ+ZGCnyPYj59ILcasG+gzvLa2c9IqnUI96OxHpfY0g8C0GXoToh+XAeIh\npjc57wrSLp3bywPGvcAroxc6S9wZ6I9zGPAyWdbsHTdJmyEKw41MKW0WNRNwFD1TEnLPgaSnchLS\nnkkdO3xdpT7X087yz5pug4oJ04dAIFBA+0sKSQ9Ii6tZ5aOi3RAVKn+miLpmK/3mhMvqbHKGZ6b0\ngH73rcvqZ97HveICl5Wu/4W8Rj77ASBl6PijtFp0BmKl0T9VcrGBlkID6ZbAsDw7a9a5diRP9DB2\nyVRy4yfjXZPSgP//DKYBDJJCIBAooP0lBSXqST8Pg4vc4jJZdbxM0m61MCg1GwZKJVwBZ87ZLtuJ\nc8qQlK/4Y4ale3Q00K787wDxeOQwaXt4cWS61O1Vt/Aa4nIYR0v7H975k25sgdZHR3xVGM+BHtUT\nyfO4OotLdA5RRtj/vB6XgBiYlHgFVTNprMUMEySFQCBQwL4jKUR0wYCzFLBR3Ix/Llpg8yh0VPKV\n05KniE/5zh2Q12NIuwGY+Ae33P8j+exR4j53fwAsRwEwxkJ6eUw+U3OJpq3XAwbaC3Undr81I8Ab\n1fogJoSfd8BOWbe/PBvZcRiX5ygvuTjyKmG8Z1aqSu0DnYJWdFYTXyfcdiQA9mUvdKvk/pt7+2BC\nakfcKqGmJ+UACX6K8vJ9g/tFDDyKbbJuk2vGOqBHpg03ix06/znokSnKsEwR1nQT25Pc8Y3c7ufp\nBV4ln/nThiQa3HJ6mW0CrYH4swzvce2iBWyR+esynQ/0jMFBMlA9I8/O5HNgZYo6IVOPSYmV6f8S\n8TN0XtOuPEmYPgQCgQIqKRv3dZwP1VPW2mNk3aW4hFMTOH/Et1trd8hn5+PykE0B77fWbkw9cNVc\nQqSQ4eKUr6BiuIEpZxZ8LutG7yVGelszBR2ynTgg7SLPvGjfq6Qd56jI8UQSqERi3MVw8ycKTzlJ\n7HDSeYNrh3qhf3vi2pxf+nzmAntSvuNXEv/vStkm0JrIA7BGYg5+uYNlv3EOcPYgUTT25nky52Jk\nDuhz8dcdD3eDlWdtXOua6PMyhzi+ZuaoRFK4gThIU/kpcIy19licof18AGPM0cBbgD+Tfa41xpRL\nOhAIBFqMSmpJ/ocx5rDEutu8f38F/K0svwm4yVo7DjxijHkQeCXwX/Vfaqm6C7peIxyzcPJvAFiy\nWWMTJHIstxMyosQZd73zvJE8TErfqArK1ISrIp0MfiKe5uXFDGUuIjJJZUQH0XUGIOXsIzPV/4mv\nMdUvWwM4zkg5f6C1UVFRXqld+8NCcX2fJ5JAd5aDJp1+YTQrv3t2fxhTiVDzbvhVbWuMm6iDRiga\n3wF8T5aX4zoJZZOsmwFSfL+XS3GPwYWu7c5Alxh/14hC8NZHwIh4l0zrDsQFQvZzzQAwKCHQq/Ul\nvhFulQfASkfUAdws4dSnqDZZa/VeTJx6W6csEHcGavO+KOV6Aq3FFxP/yzNx8jj8wi3+/gWuwzjG\nGEyXG4Cy46J87BiHE92U49cb3L6vUE9IMnG2aI2LKJU9uoHU1SkYYy7Ezai/W8O+ZwJn1nP+QCDQ\neGruFIwxZ+AUkCfYOFPLZqKsJIDLYbaZFKy163DxpXExmIYjJsYB8QEY6oNxSW6iee66KNaYFKC9\n9t9Le7E3zTgo2upxGSEOOVmkjdu+Bp0yDYlSxl3q2uH9IHOHrNO0Wf8CHCfLKopqVOVbyl1gYFbR\n6Z8qwfV52QwZJ4HedrurWHTMiScyaZ2E2vPoM7Lds/ALJ0m+Ylwkyy4vNDKZjxEqqydRh39DTSZJ\nY8xJuKd4jbV21PtoGHiLMabbGHM4sBL4v7VfXiAQmGmmTcdmjLkReC3OZWsbbjJ8Ps55WwIJ+JW1\n9izZ/kKcnmESONdae8u0F9E0SaEMOkfrpjiVQUENCU3AolLBJHBkYh08K/PAJVlRas7dBHkxT2XE\n8az8PyYAAAbGSURBVGn8fNfmroTs62RP0Xec9ASxI5MKcJrirbLKPoFWQCuEdRLFPPxeiiTPmY8V\nU7f5o0RJdm2FUXkAreqoNDnL08AHZTtZlad8vmCNl0iXFCpKx9YaORpno1NQESxN+Z8n7hgGxYIw\noKHOc4heZDST7zhxfLa8yP/reegREVHdVqfka44bmJSTqZJzz1IYODZxkf8m7d8TaCVkGsgzOP8Z\nH1VMn4PL4AXxg7WEyA1acygO74A1+pYX+rPAP8SHHUkcCrx6qxQ+u1CqOFLI0RgIBKrnT1dS8FGp\nIYpzugbGEuHR/W6q8DMO4PWRHVmlA4i66o0iFcyZB33qtfi0a8Zz8SEnZFSYcBLIhqnlrDpZ1TMa\nXqt261GKM3C8m0Cr81lpfX2+esqqsno30TRDw6mj2OkHiYLz/GdUM0hPzXdt7v3FJoPUPI5BUggE\nAjXQIlGSK3AFMT8w3YbNIamUuXkJHCwVoXbIiH6Lu1Wvz03BGs3KrBJDJ6wXyaJTJIXsIuxSp5A0\nu2VUyMvIn98LRiQLiZA7emoBVnQUJtIqneBdlPqH+dJJoLU5v4JtLiKOg1FJQWNmvBB+/xm9WXRZ\n2WNca7+BC9UHJv24oNoIkkIgECigRSSFTcyalFCAuC/nDsHuL3HvGedsYp4T62vmSSKT0fp/lHVX\nAWJ2ysko3wXMkTnfhOgDcpp2K0fUH3e44x82OcGun8p2b9ARw0d/qr+T9hJKx4ME2odPTr9JklOk\nktgGKU6cmYyH93zqHlXRIp3CCpw9duYSSaQjYar5PHf3OYPvn6nu72kJWukdBaQziMyZ7yfygR+X\naceJT2K2SoZmKyK/FUVjNg8SQsv4Q3KMp5hnnanzGFFC/aHg2lTRGMyUAWHMKzOo+kt5o7VQ4bdr\nOGyYPgQCgQJaxST5NE7b8sx0284ASwnX4ROuo5B2vo5DrbX7TbdRS3QKAMaYOyuxoYbrCNcRrqO5\n1xGmD4FAoIDQKQQCgQJaqVNYN9sXIITrKCRcRyH7/HW0jE4hEAi0Bq0kKQQCgRagJToFY8xJxpj7\njTEPGmPKVX5t5DkPNsb8whhzjzHm/xljzpH1i40xPzXGPCDtoumO1aDryRpjfmuMWS//H26MuUPu\nyfeMMV3THaMB17DQGPNDY8x9xph7jTF/ORv3wxhznvwmfzDG3GiM6Zmp+2GM+box5iljzB+8dan3\nwDiukmu62xjzF02+jkvlt7nbGDNojFnofXa+XMf9xpgT6zn3rHcKUhfiGuBkXAnm06R+RLOZBD5o\nrT0aOB54r5z3o8Dt1tqVwO3y/0xwDnCv9/8lwOXW2iNxETJpeecbzZXArdbaFwF/Ltczo/fDGLMc\n5yL6cik+lMUlqZyp+3EDxVk7S92Dk3EpB1fikhBf1+TrmJl6K9baWf0D/hLY6P1/PnD+LFzHT4A3\nAPcDy2TdMuD+GTj3CtzD9jpcmleDc0zpSLtHTbqGBcAjiJ7JWz+j9wNXEuAJYDHOaXc9cOJM3g/g\nMOAP090DXO6109K2a8Z1JD4bAL4rywXvDLAR+MtazzvrkgLxQ6DMYK0IhxS7OQ64AzjAWqvZTbYS\nRTo1lStwiRg1nGUJsMNaDZqYkXtyOC4bzDdkGvNVY0wfM3w/rLWbgcuAx3FZZnYCdzHz98On1D2Y\nzWf3HYDmP23odbRCpzCrGGPmAj/CJZl93v/Mum63qeYZY4zW6byrmeepgA7gL4DrrLXH4dzOC6YK\nM3Q/FuEqjR2Oy4zbxzRJ+GeSmbgH01FPvZVKaIVOoeJaEY3GGNOJ6xC+a639sazeZoxZJp8vA54q\ntX+D+CtgjTHmUVyhh9fh5vYLjTEaxToT92QTsMlaKwUp+CGuk5jp+/F64BFr7dPW2hzwY9w9mun7\n4VPqHsz4s+vVW3mrdFANv45W6BR+DawU7XIXTmEyPM0+dWOMMbh0u/daa/3aX8PA6bJ8Ok7X0DSs\ntedba1dYaw/DffefW2vfiis6pjU6Z+I6tgJPGGOOklUnAPcww/cDN2043hgzR34jvY4ZvR8JSt2D\nYeAfxQpxPLDTm2Y0nBmrt9JMpVEVCpVVOG3qQ8CFM3TOV+PEwLuB38nfKtx8/nbgAeBnwOIZvA+v\nBdbL8gvkh30Q+AHQPQPnfylwp9yTIVxG0Rm/H7him/fhUkp8G5enbEbuB3AjTpeRw0lP7yx1D3AK\n4Wvkuf09zmLSzOt4EKc70Of1em/7C+U67gdOrufcwaMxEAgU0ArTh0Ag0EKETiEQCBQQOoVAIFBA\n6BQCgUABoVMIBAIFhE4hEAgUEDqFQCBQQOgUAoFAAf8fWidT1nvXudwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9bea66c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_tif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.61710000e+04,   8.00000000e+01,   4.40000000e+01,\n",
       "          2.60000000e+01,   2.00000000e+01,   1.90000000e+01,\n",
       "          7.00000000e+00,   8.00000000e+00,   5.00000000e+00,\n",
       "          4.00000000e+00]),\n",
       " array([    0. ,   186.7,   373.4,   560.1,   746.8,   933.5,  1120.2,\n",
       "         1306.9,  1493.6,  1680.3,  1867. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/BCxJsriNWQ7sb9P7gRUAbfkrgGf761Os8wJVtaWqxqtqfGxsbIjWJUnHM3A4VNUVVbW8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\n9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa6f86d150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(img_tif[...,1].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tif.min(), img_tif.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188, 188, 3447, 149)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tif_adj = adjust_range_ndimage(img_tif)\n",
    "len(np.unique(img_pil)), len(np.unique(img_ski)), len(np.unique(img_tif)), len(np.unique(img_tif_adj)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ffa678dae50>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R4iQf9UoM74U/3OSW98go3/k85KQSUtbL4J39mmv/j4x0z/y9dyCtuHyDXizVJfj10BG1\nh9iMV1aXMA2qr0g6O3UQO0U1Orms6np+Km0eT4ErX7Bfn4PqqblTMMYcDHwLJ9NZYJ219kpjzGLg\ne7haYo8Cb7bWbi97sCM2wxcuqM+u6/splD2OFBrtlxJ1ty4lksP+5ld1XECr0Tf9JgWIF2J+jOLO\n4DJpFwD/VN1hj3mLa/Wl6SL2XVCd2PAXiYLRDhYPyGc0MGqC2IflDO/Aqtn/grQ5yk77ki/tUmID\nUyMSmusxtKNZTfwi1xPWn6zA2kVhZ6DnjLJbf4R6qWf6MAl80Fp7NHA88F5jzNG4X+Z2a+1K4Hba\ne4IeCPzJUbOkYK3dAmyR5V3GmHtx84A3Aa+Vzb6Jq5xSYff1DSJNzaCk7Ko0/NlXNJbtmdX0JjUe\nzAIYl+79B99z7f+s8Jwtx/uBL8rys+U2LMYmXQ99XMSi/f1+mJfUeGk6kvk5F7tFmZhfSGTg36Zp\nx2S8Gp5KEb+vJI6iVMVamR/NH231a+6gsOBMvUQ1HqTdSLH0UG0RG1/69aOfk9dtqzzuNDREp2CM\nOQwXZ3MHcIB0GABbiVXGyX3ORFXj+6VtEQgEZoO6OwVjzFzgR8C51trnjYm7XWutLaz+hP/ZOpzz\nPWblCkvXPwMvgXtkTvn0j2XLzdKOUbEzSNm5m8ydN6yU/5dAXhxl2k5CSKZcW0elyTljRKloNUtz\nmqTgzFtmT9LbpwpUmWeI811skEmxzYE5zC2/XLzvNko+i4m0bNPnAJ+Q5YOKP04mgvFKz22Tx/OA\nnRRKL41CH/8poFMqYXVqCrtPpigfP0GkT9H0dL6EoJKNTvRzxNdd0ju0PurqFIwxnbgO4bvWWn2L\ntxljlllrtxhjluHSH5fHboaJ84FXwUL5hbaIr+yQXmJP+Ze9ooCoL8OQKBUn3Z2dnMjR0V9hwE1L\ncSXxU7FO2oXEJdkkkCstP2Eq+vQtodg9V2z7x1dq0dDrGSXyhdBpoP87rVKF5ufgzoXePkBmuic+\n6c16DQxJfkIjj1y3hBNbopf1AO2cpog7qtQp6melTckgXo7IkrEWJLkJGfGDyQJTksF60GVG2kOW\nPqO915WyvXihZj9a2MlUdN31U7Oi0TiR4GvAvdbaL3ofDQOaWfN04Ce1X14gEJhp6pEU/gp4G/B7\nY4xUFuUC4HPA940x78TJnW+e9kgPIVLAfxDVDDhBEnxoeGs1tuQiRaMotAbnEYtqbkTq6H+S2O++\nHdAR8hyiwq9+6rVBkRB0FFEFVZ5iTzggGheWiglzTi88qmHXyRJolcbgqhIwJR9aqrT30fhJTJrg\nKmQTGVZkJS9hVtRYeswp4kv3C7mUnWbqhmryTH9G/k3ayKtCjzm4Nt5IryML0fOddWHsffZwyIsn\nrfmjtFLItuNSmBJpTac4drrrrp96rA+/pLTe9oQS6wOBQIvTgqXohUqr/CRTU3UBe9VB6UhZqd1s\nH3tucYqpvh4ZOv7aopGCsMdr20F6uNE1G2XOOrkDTpHCrFEMiMzvc+OQFykiZQDfuvwHABw4dwL+\nIPfh1GqVllIMFS22+jxxZa33F2+ehjoZabmICeJRfroUZyNy/h4Rhaw4PflV6ZSSJjxx1Lr7la49\nVhXdW0gmYx1nmmokSamn+2owIoX1HO/aQw6OdbuPqKSgUY17YVyjXOUmrHmOWMlaNaEUfSAQqJ4W\niX1IIamtHiKeC+uI4adzL4iOlNRe68UBokf6vvE+svNFQx3VHdwNA5oZQ4enLHFSklau9yCRgvPF\n4vB8F2yU5XFNIyfz+8xovFuB1dE5cR34qIx53aOQT9aHqBSNNlW9zR6mreyVJJqQyly+Kwfj4hSr\nKdZPLbVzwnzpf8+KnHs+jTrNjB/kHrbu6KHrLdp62m+WdGga3wFGdAoHOmns7mV5XjIhKdgfl4dy\nrz6Pi4mk1zXqjKb6mubRup2CkjZt0CnDRGIZXIfxM5EVO8TW3SM/9LzFPGsWuOVDXUfRfViG4nLC\n47R2Z5BgqzxMA2l5LsXB/w9TcEzazvLQj2jW5U7igiLVIj4g0TQsT2VVrT2sdmpHuLZ7N3TKtCDV\nZ6ECKs6svBQtrdc9piK89ix1PA9R5zAvLpiz3T2wx07eC+NSdGdcFY5yztVdFKem1mc1iSaBqT85\nUJg+BAKBAlpfUkgjzWnDd4qZkkg+DccVAaDbLGHvctcPdi2WHntXB8VlfropNse1MAPlRjFx4Ck5\nI9DvrB6N28C8t8S211J+5K9xJPdZLdOekf/l2tx86BTJw4r35fDZ5acDRWrrq4kf9bRwcDU7biEq\nUtsUf7Y89Mt3+alIcM/ugb0ytcvpNap0sotiA19awPEVuAhWiHNnarq8LxBLcGsrusogKQQCgQLa\nU1JIQ0eRwU4Y14zDEnE3KT1xx256esQ8qQOkfZbiKqFQGJbW6mi6slIjPM7VrIivAYfIsigHx5ck\nCsT6NEASmBZxpT5EFMK9GXhMdBxd8jtNXgqD4tST5j5dxBzK50fT3BP7EzmHvVTNfioxjlOzmVqv\nbQ1Ez9VeGb1/txemJCHKgOoD0pLEfl7aC7xlrZR1bnzdRebKBaiepFL2nU5hIMpxTWQvH5YfQDMr\ndT7P7kmnxZ03Juv2PkCszFF/hikq9WhrDcrnsClGK03PQVO8T77MeQF2/L9xGNe5hiY60XuxhTps\n5BXiOubfy5Thdx0TvG1Kfp8oY/KH43d8UPwwskBW/CqiACctWLMfsQgtIfMFyWJkysC1xN9P4z/U\n5yJ9/qV5YnrSPkxmXeYDOIdfYI2K+/7gU65kolo/riFWNvrZp0r9LjtKrC9NmD4EAoECWtejsWau\nJi6ZJocdEuVO/3Ie/KUbEY/Mi6fYrofhFDWhqXh8HXG9hApLircVGu04FwZfCoA9wZltze8fgu2u\nKC2nqsLLDxioMh1brdwpYns+C9tEOegPqrlvuna5+KR0TcBjd7nlVctdu0EE4YklXkZjzdg9Sfxd\ndPr1NJUq46ZlkFgOTzWJ6hSgl+pLE4rHLh+r9qqCR2MgEKiefUenEOH3utKjZtQZZ4ovvFp8yDXB\nxykTFCvQ6sxiPKtoFLtOus9J2car69Zxn1vcK/P23c9I8laIJ+cafFBlird6eLlnCtZ4iKw4Nk1N\nQadc4xLRFXTn4c/WumX1bu38lmv7Dof/kkd9qzgKDWzD5R326cFlxPbRkmtLqOq5mPbN+pfpNihD\n1RJCVeyDnYKP3LzI1HsD16lwtErdfvPEopy+BDOYar7haAe4pcw28v0GPw8Z9wh8+DFJTJJ53ktw\nolaZagOjfFSpWUNVcUXlWSMdfsdXiWpa6tf0jQvRNEN2PHgU5oo1Y4cUpxmeC2selu30+2YpDtxS\n3+o+XMpRgCekTXk5dXMDnJL+derhAS5lZUoGMr2STxd9Uj1h+hAIBArYxyWFBLdsBiOjwkmqSKww\npLdtUPNaBanTBhbBzW5cuOyV4sK3YYJYtNpfWjVNvr2G60lLq5ZIGVcpajk++V2ueADEYRZdwB1i\ngsxJqJKRx3vnXhiXdVNisz90LrGZ0Tfpiskwqkyg0lIXsRSmuYg/hebJnBIFZfZUMWEO9hAXlmhc\nseKVzCXNJ6EREoISJIVAIFBAm0sKNxIn8aigTNZ4F3TpfLCco8i+wFnTb8LTYNV8J840+VGY1BDo\no6XVRCM3EJv01Io83WifFjvyYmlvk/Ze0hWiQlLYGKQ4A3MX0C1OPZ0iDeSk7XkRdDnnH/tX7pE3\nY8CQjPj9+ho8jTpzxY5Pj0j7JIy4e/RdMTG+lWWM8B0AsgURtoinZSMKSiRJUXYmq19BFZGhxQRJ\nIRAIFNCmkoLOqRYSu38m/cGJe9AOSZhiF8N4dWW592mGLiBK19Ypo+w4YN0IZ8WHyaxQ1X4ncZyA\nSgqfpeo06JHLsT5+ZaQEKM6pMQJMJNzPe4C8XFNW60m4kd2u6ICMOCKLZcJ2TGB63fk3bnDf6cRV\nOWI9g3znYU0YMwGTTi/x1iiKsYelUaoVNYOUiT+pGEn1Pt19URrsf9imnYIqWK4kFna8dM/JWgcd\nIhrbHlizhD9Nrie+V/LS9Fu4RcyNkaR7Lap4M936oCfrn/lU2yFAzQUL9HedgqJYlNz1MC7fb448\nC9b1AKbHYjtEvJcO78fZPPS4qdCJVoq13LoIJmVeokF0WTlWPgsDvhkb4DT+soHJTWIq7AyUBpaM\ngzB9CAQCCVpMUriMWDytxHusRI+q30pHv7yMEqvGSB/t9iU0t6SKvV4cyEYxqRkZ8d64K051piJ3\nxvPu3K9cKG8aagbVe5xm7r3Cq/ql1/g82ArCsjU4sGAoEw/EiSzME4lmSn74Z933tWMT/M85zgPy\nB7Lz34zthQ6dxsgBe7ZDTkzVO1WykGOuzpCuOGykhNAaBEkhEAgU0IgCs1ngTmCztfZUY8zhwE24\nIeou4G3W2gqH5w8RO4/UiG+eUXfXcZk3j6yD1eWSbbQ7a4BfybI63/xM2uOwS1wm4N9nDwbg2F/s\nhA5xWuqQETJ/PWQlBn+9HKNk9uQku8t8dolrhhZAp9bj0JwCfwR7eWKdH28hJlKVJjrxhjPNbjwn\nrrS0V/QCc50p1WzJwiInHRnVETw8BRlVGIqy2uyJj3uqnl+flwniNGmaxGcmKJU8pXnUHTptjPkA\n8HJgvnQK3wd+bK29yRhzPfDf1mpapBLHONJYLsNJZ/0Sxjqkot1H4w2LMjtfAnzELWoaba/CcFE8\nD9Rlv21d0vJJim/Bf8vvu20HHOqCe+zB7sWb2Jane7O4BKoSPX8f7NKcgfIS6n2cpPaipiM3uLbv\nUNjPTWn2Hu5+mN5fbYW8BCrpHOGN8vLetjtepxdizvb8FOR56ZwDPRIktVPEfFEqMi8DC2SHLrFC\nPHwAdKkno9tva5flwB0yTvarv4JOGXYSJyxJZqRqG5ofOm2MWYEL+/iq/G+A1wE/lE2+SdMr3wUC\ngUZS7/ThCpxjgLrALQF2WGu1H98ELJ/2KBnifFbrRVzL6iElfdb4GAxqz67KnSPAiJ1qjUT5bTSQ\n2eaW8x8vPE/SC26fQUOh16ERjfYZ8ebrdfczvx9knxIRetfj7rO9eyEjYtWU1sogVjYm68lWPfP6\nEgxKlui+F7h2wX4w5X7b3t1q4suAlfEp/6hrhyUbcanCwlHBWM8vQKV7k9jmf3j7bZD21G/AsObr\ndMlWDswC/Vr6Tv0VNL9hN7FpVr0dNxOL9cnUdfVEljaam6R9S0Vb11OK/lTgKWvtXTXuf6Yx5k5j\nzJ2l048HAoGZpt5S9GuMMatw4/x8nDfRQmNMh0gLK4gd5wuw1q5D3OnMkcYyjps6TorPvo7qvZ9y\nbacllhAkJiyb40apV3DaRlew8/aluzhBz6gjRqRHuIbGeJy1GnpfbohXzZFhssPdhIn5HTAqEXz3\nSS9sM3Jfgd2STGT+ZDwgJtVNHcTJSMtNCiNlbxdkV7rFQ53AaBf3wIRILDslaYvZGXsjToqEoL/d\npHcdOvL7Q1lU+p1Y36FOTpH+41tEyVJWqd7l7bBGayQI64ENKiWJxHCqnnw7sU5BTanziElLnqtK\nQpGS2MXMZMSuj5olBWvt+dbaFdbaw3Byyc+ttW8FfgH8rWx2OvCTuq8yEAjMGM1wXvoIcJMx5tPA\nb3HFBcrzEK7HH6J4BLpF9AK+38iUTDQ7HuQ0MWHZlc6sZKaeh5fJ1xrWNN1SaJaltEfh2GrR7xQr\nTX6ZdzqWl066fn9eJge98nlGRr/8PB7KOEvAEbl73LqJLfHInPTVyRA/MWrtWQMMX1e4Q0akvayF\njJg6e52G33QBqKVBdBtMwJictMi6cTXpkZiJnAz+fkXHeJr0R/3Cwn9PJQ7czMqIPiym0jXnpezv\nk0zw+3HgIACs3BfT6CCFirmnqq1bN5uzioBpTmTacYyA5tnLvfJwADpHH4XD1Zypoprm3TsEeEyW\nq/QvbwuuIvIivE/k/HlaM68D9ogm7il1G+mEvGRDHpP7MjkJVvbJOa9SyeLIi/xTqSh/CnCzKNky\nago8Q85JXKTnKPEF6DGQk+u4V44xZuEUfQS0Y5uFcn3DVxI9F5pPRZ+/E6o92FeJe1fNBDPrA1HI\n5hwIBKqWmcTdAAAgAElEQVSnxWIfPCp2knkAgM5NMvo8+TSx772ai57z2rUNuLhWxYs1eJEkB1kv\nyrDOhXCi3JdfqO1uL060BiblUTDPwbiMcGImflEkx2kSVmDcT8Qqmt0usVn6ispxiWG5W5TDHR8j\niqUYFwXpmjit2ayy5hwi5eDN8uyoU9ftVCktPElj4iI0O/fM1R8JkkIgECigdXUK1fJzafewj7oy\n18s6b1mkgpFRorl7Wop0nU+rYLHq0nibnxWnGS/aHopTMfjnaXAegIaguqzkPegGrNSJeP1DsvJi\nmo/EjKg7f31UpFNo705hkNgT8mRpRwidQgpbgQOj/1Tx6k03ImvCVXCrrNeQA60N43sX6gt9a8rJ\nphKtv33L8EXiL6h0EV203g+1QmR/DJ1Omc0esZqs2USb+b0ERWMgEKie1lU0VkIHxd9gWilBReAU\n8Xcf5sCC/1KSn0Qj+XPxCN+ZaE8hHkGVckHxLSMdXA6U8zNQ+2ovkeJ1jZgRo+/7BBzo/A44Qbfv\nIDU3aJsTJIVAIFBAe0sKvslnRMxcZSWFq4jz+ktMRSuYwlqKtcWrfOVgMnKyZaSBcnSlrKvQxKff\nd+ocyGsdJh1Lu9mXJASlTTuFtdIeBBwly0dIu5nSBWIXE4fCLm3Kle2T6ItxM00pmupopvv52PSb\nRFwmreQK7fdyhQ5JGdeb5f+m3YvZJUwfAoFAAW0qKWhftoLtWReSu2g/dVbPEacn0xgIrU0wgcv5\nB1EobWB6ImXi5yhIj9cwvkRzSqwpfcQlWFPKxxfwIdcMScCVxtmsJ647VLaekEoaH6rqCluJICkE\nAoEC2lRSUJvZfSyaL933Mi0tn6N0Ha13EJskG+Ihtu+RrK5VwBJYnyjXlqP2ZK4Rz1FDrrcqqKTY\nLoXfvVNCstfL/6fi4h8g1luuB07V+BotT590iKoEVZjrc/upUhvOCEFSCAQCBbS3m/MIMF9MivOl\n+z4umBjrRrXr6kIe/TpXEA2TrxdFw9C57Z+vWyWETkrXuLiNYmHGXgV7l7nl1VJ/4hbJRTH2OCCR\npGUlqc8SW8TSksk0lD+B2AeoLGdgoDq0U0iT6DvFdKg63Oy57R9rojklMxTLzuazsuAV0u2QxDE9\nXZB/oVt+jez479IpvHYUhp90y2tUM7mbOHtL0zuANELsQyAQqJ42VTR67MtV4GYLLV2Q9F7MAJ3i\nXKTmuUnikbbdJAaVMvW6B4mTqkTPlc6h1kJesjhnZaPMUuiSMnd3yPRhQm/aHsgvlmXNOzdFFFvR\nwgRJIRAIFNDekoKfT6FdR6tWJKmf0RE1Qyw96JPja4P8DM/tQHJI9BWCqlfJS3RlD2DFHJvVBLVL\n+LcD9gfgtDniFr1Xalr81y541kuQCzgnqpQI1RajvTuFAeCnslyqvFigfnyvPn3OVczuJJ5uqFNi\nOyh/h4inCLdIezJxZ2AlN+JqeUVufT9MSo84KQ9bXxenLZSeRYrbmo5N7v95j8AOVTBqhur28KIN\n04dAIFBAe0sKgKtUhzcq7Yv1HFqESWJJWAfBHoqnbDraphX3aRV8x0P1ULydOKByUkOrJST/JGB4\nt1veLVOFQx/EdDvx6Od5GV8f3uLaPWNwsopVOuearqBMaxAkhUAgUEBdkoIxZiGuFM4xOJXTO4D7\nge8BhwGPAm+21qZV36yfYYj6tWGZ0LaLkqsd0dJ+UCwxpNGKcuiIt9yd+Cz1u3iJfNYksjf/b8C6\nQrSv0++6XaSC3ZZim+4VRHkaIpFkVpyYylKXR6Mx5pvA/7bWftUY04Vz17oAeM5a+zljzEeBRdba\nstFHdXk0FlUYjtGeaFHNBw+UxO8cVMmrL5nKnxPAKS2SwzDZmXURV3Oz3jr9LpUMLkPE/hrzpdUy\nmSVdm78urSofNzMzqeKBZns0GmMWAK9BCshaayestTuANwHflM2+SevOKgOBQAo1SwrGmJfiKozc\nA/w5cBdOy7fZWrtQtjHAdv2/zLFmPwAjUDuqWFQROifxAp19cOJyWamefBWGMTeSEeLhr8Nrx6VA\nzioRD0bOrt7PRacjelyVNEoFVs0uTY996AD+ArjOWnscThgrSMtjXY+T+sIbY840xtxpjLmzjmsI\nBAINph5V0CZgk7X2Dvn/h7hOYZsxZpm1dosxZhnwVNrO1tp1SC2z+iSFz0grPujtVbFn30D1aTpN\nPlUm2rd0ws1S4n5KFGsNVwRrkpMy8/KuS4nqfPgjelauaUTcYmvxhtV9VGfRowlSFhGV5CuLH+o/\nu8lVlJolBWvtVuAJY4ymUz4BN5UYBk6XdacDP6nrCgOBwIxSr/XhpTiTZBfwMPB2XEfzfeAQ4DGc\nSfK5kgehFklBHEpYDMOi8p4r/VtuG5xYKsV7oKkkq0etIZ5zW29dKp+TNpkY9mriAJd/8tZrWj1J\n1T+8m8i8p+7LmoL9doonsWN48/6vSTsBnE1NRJKCtCdVuuN3iEWsM2o7d+VUpFOoy5Jsrf0dkHaS\nE+o57vQc6pqNR8AS0WH2iAz7zCPA9bKdeKBFBugLm3tZf+qkvfCrE1mU1xO/AwV2qVJZot9H+tRA\npgNDMn3MzolffI3BuF3iF2wWxhOerjn/n3eWOHe9aGej86u5gOYSfYe0m5t07toJHo2BQKCAVvQ5\nqwAZavbPMbXceaNketzwYCbnAzsS22sO/ssBSZ8V6T+3UpBqK1A7RY5knyb26hFOJTZhlnE8K+QT\nZT7zpoo6HfiZrpDnZKIPkLDn3AcrPGedDAIDaolXs+wOYpvlJdK2XlbxICkEAoEC2lRSeNw1z4yR\nXexGAztPfcq3EusQktqlvfDfkjn3z9U/NQvI3LPSoqOBdJKj7/DH4vm9b+5TBeB6qkQrf00S1Zz0\nn+CN0uYulwWJSlxV7XkawMDnUTO53ewyPputC+BlGvOgJvSraLXEK+2fzfk317h2hYhq922F13yw\n9Pa/+oprj9cfZRx4W82nD5RhmNgSoIq9NKf3Ear3EUjGMnR659L3rpo4hnrR6/E7qVPl2Xzqr1y7\nx8Lh98iHGniRozgppg5OV9PggKmQzTkQCFRP+0sKyu1aj+Bc6P9M4sPgtzBr+DUVwLkC6Ohuvc+S\nUcZKKQkiKSn0EJs6tS0nnTSbYWIJ5bHvuHYCWKkFjlWk2EVsjlWFqoo8hwHPeNv529REkBQCgUD1\ntKmiMYUTRPF0C8BBsjL0ebNOFBsg8+uu9xZGKoIb0ZP1O1QfkPSSLIdKCCqBzGbQvh8XfOg/VLhT\n0knrC95yKVGq8YS3JhAIFND+kkKy1sDJwPAZsi4Um20Z+sVMPHIpnChD+k/Facx/CtWEmawvkUQ/\nj7RRnyFK/TRZxvo0U5TNIFIps/M99gFF479KK3ENtwFvrPeKAs3jcuIcZpJw5Wbv4x4qIxldczsw\nJoNATkKQ99mcX9dK69eT+FglOwZFYyAQqJ72nz4kIx9zwK2yrE4s++yI0Y6cR6RAU3Nljti0OFG8\nR2Ra7Ez5TJmAVklSAsAGyme6rjn24nPEhSo0ruQI4lDyD9d64IggKQQCgQL2AUkhwSnE0Xf73rdr\nQa4iHt4/VG5Dx62AEW3i5Pdcu/rv4s/TErXo71nOKufnR5gNyVCvURWlGW/Zv56hxPadVJjkVSWB\no4hFJnVo2l28eR3sm69NUjRrh4KnbUuWqgTOTmByV2LlJURvdRSnoG/5xZWJ2rNdBEg7AH2jxkh/\n3nTdkLeuoorpeoIsMCrL6u34noovsxLC9CEQCBSwD5gkE/gRd9ob69GbnVgjUMggsU5MRf85xApg\nNSfnLoR+qcHwK/FGPV4T5TzADFZQqp1kLMYppTYU/OlGpfsAbrqmN7DqqlvBJBkIBKpn39MpWOI5\nmjrCvKHcDmkx65fQimmy2hNJotsrw+HUdsiIQjIv5uR+iJRmc1VpqY/mMu9Y9dSlVIcf1VWcU2rD\n+pjylsulmxvwtsmlfF6S5idk2femD1DcKfiJmMoqczRjz2HATlnWUNcWcJ1tG7RS8zK42dnShzrc\n/KH/xIfhdgn/VSn4FGBQX3jpFIx7u37bP8VxUUr/FkanD+qsuZfKFdsV56qshLXS+rkxo4xiYfoQ\nCASqZ9+bPqShyq5pYyL0dnQBR8vyNmk1P2AlpcD+1NF8mUsg5ySt/kNTiqxo6foRwMiUQG32MvIe\n14CreQBY2YDjVISZfpMi/KmE/39V6PRIM0frPCYPXCfLlRW6CZJCIBAooC5JwRhzHvAu3Gz997iy\nccuAm4AluPL0b7PWpnm0Nx89a1fZrTy0XPoh5GRO1hl1/XdLew1xdFqTlFVtj9of+6BfKiL9p6wa\npjiWoZt4YNOIyaqUb+WZMSkB6suFUrNO4UtEku2vj3TtK3S8f5TiOijlqVlSMMYsx6lCX26tPQbn\navUWnOr+cmvtkcB2mleTKxAINIF6dQodQK8xJodzS9kCvA74e/n8mzh16HWpezeFi2EqmdxS6jqs\nn4RTy5mzVMt9BZ1RfjD1K1dx41ncVw2URitufZuodqJkOWeYWCpQIWwK2E+W1dgTcFSkZzDwoDO1\nbV/qxvmFTztp1uyXp9rXvOZOwVq72RhzGa4yy15cepO7gB3WWpWvNxFrPmYIA/1iGx+Rwi9TIkB2\nPwsjV8k6KYTdvzblGOd6y+pNV1cW3T9RHqNInvYrUev0bjXwf2V5jNK0YgyLXlMyx2SjKNcZaPDY\n1DbIunoSi8yTslLjS/y6EpVRz/RhEfAm4HBcptQ+qijAbYw50xhzpzHmzlqvIRAINJ56pg+vBx6x\n1j4NYIz5MU5IXGiM6RBpYQUlam1ba9cB62TfxjkvDfrO5E7psueIwwDoYwn8cbt8pp5NVxNX60kr\nhx4khNopkSIszYFMn5K0kVGT5uiAlxbf0g9x6Xf9PWewHJs+cs1KujzoLScjMg/8JIjgS04S2Lyw\ndme7ekySjwPHG2PmGGMMLmvePcAvgL+VbU4HflLHOQKBwAxTj07hDmPMD4Hf4Gx0v8WN/DcDNxlj\nPi3rvlb6KE2gw4BRX1M3aZ2z2JtTdUmXPqaKQ0N8Gz4rbShNP+MkJQQ/ilDlyHJP69AVwFK33N9A\ne+Z02EQ7kfi/gI/jhGeI5/wVJKaBwvuj90bd9xfgcq8AjXDHb5HYh0MsfBCGzoV+NVSo1n+6nHOS\nl2/ES+feLYVHWOyale5h2TlnIQvufsKtGxN5a3U38S9Zznqq/vxt4Ie/LzFUYn1a1ucxUSJbscsP\nXJSyUZOZVhlaT1BXCW6lTD7StcTxECH2IRAI1EBLxD4ccWSGL1w1h/4MsEFCZcdFWVTOJDME9Itm\np0uLyl4A5r1u8Q0iRdxxGgALeq+BN6hkpMbyccrbwa6v8FsEmkLaiDtEcdbnLqBbFIvlsig3kkHi\n51M9MadNlNJACUFJtfmtlXZx1YcLkkIgECigNXQKKw+2XPEBYApOnSdrRVk0IglQVhM7veglG+Ju\nrcvbrdRI0UHsZOIrhlKlkSulDfENM4PWbKiy1J/O4X0dw0zV+xjyzqFm04o9dWaFinQKLTF9wG6C\n3AfE/prwINSXfshbnvLazpR1paYcI4CRvICdkvUn1a58LXFc72XSVqglDtTARcAiWb5C2nNLbFuG\nkp3BRTRNQTwkg8eUDB6RovFTgA5wNXyXWSRMHwKBQAGtISk8hJdoIuFB6CuU0pQ4gynrSjEFsL9b\nzkrqtcnzUjZ8D/GINZXyeaCxdBHXMqhxOpsnRUL4smtGpuKfsaFTim9Dj1xvkZTycVyJt/YjSAqB\nQKCA1pAUfKpNNFHN9v3AkCRSsRdMs397zQPbm4rKqKfjK4xvTnyWkShZ09Ukge8pyr9CabE0rU+Q\nFAKBQAGtYZI80li+wMzGyftp4MvWhQi0Bb4jkTJ0iWt75sCYKKf6G5iqf5A4pXtSp5B2PbNPRSbJ\n1uoUoAEdw0XETgtlxFK/U1CzpPo3VF2s9BpiMfLd1e4cqAvJqhXXNoiJfBi+xPiYy/XT3a95OM9s\n9oW1IiH2IRAIVE9rKBozOF+hhvisT+ekcqm0En2ZI3aAinL2f5zYw64ca12zfjnYQ91ylEDkeuCs\nCo4RmB79zVSq9eMHNF/mNcB7S+w/TmeBG2y7o2Z7/S5rG3r0ICkEAoECWkNSgBnsnkRCUBNVFpiQ\n6k+rRWt0q4UJcTxZU86sJGXTuxeT/7MDAcjc6xJo8uLfAF+X7VQEUm1UmsNUoDQqDaTpv/Se7i2z\n/wfJRO7zWrD2M8AFDbi2EgxT7ELfMEX6xdNvUget0ylYZsj6IEku+kUEXf8ZmHyRW77jANfufcRL\ngKuh01pQw+skVJF10Cjf63Ev/lif83fvYX+cHRtibz1VgDbRF3+f47Nwp8RFvFxf/C8Rl++r1J9E\nRW5N4tNXasP68LM7q3Rftqhx6xGmD4FAoIDWkBQscSW2qnP7a15FjbKbTrmXSHKR64CsE0vtIjd6\nmM6DYZdmBFbxVJNVXA3rJZy7V8xaPV/lNJkh3DP/YLdwWy+8UecommpX4i40l2CgAjpgjsrh+pvU\nkDJZn6ehswv/bzQ6w8k18RxNJkgKgUCggNaQFB4kLkFeNZp5ucrkHDq3nJeB1zubpHnMFZWlZy6M\nSeLY2yXR6wnaf2ZhoSyqdPMgML4TgKOPFalgv0lAq/XoDvp/0CdUzoej2qkNpVkeh3rMUgln24DW\n6BR8VPyqqIaeTyV+BQCfllYUTWO9qCnCdstbvrADMypJVrbKLfqZ7NaRgVdLva6bRZw95VkiZeIv\nJE/gzkqvO9B0xoBbZFmVfzORIUnPqTPQ1nN7TiVMHwKBQAGtJyk0vTdV5Z/YvnOLYYMrJWf2PODW\ndS+AUZkGGJEeNEZkxzwYEqVjXhVeWSIT2V8367oDVRMpF4GTZVlzKa4nnrIOip/KwDYqlzgrwCTa\n1gySKiJICoFAoIBpJQVjzNdxfepT1tpjZN1i4HvAYcCjwJuttdulpuSVwCrcJPsMa+1vmnPp9SKS\nwMAkkanr1yIVmAnYJZ/rfHDycNf2Z2FI9AeTWvrrbO+4qvA8AjijwdccqAnfNKjK4Q7ipCy5f6ap\naGzNDFazq4dKJIUbKFbLfBS43Vq7Erid2M3vZGCl/J1J7D4WCATahIryKRhjDgPWe5LC/cBrrbVb\njDHLgH+31h5ljPmyLN+Y3G6a4xdfRNXWh1r5IlEs/pA4Qs07HPaIFLBG/e6PkTYDPCzLz0ibVoPy\nGmIT5L827GoDDcIvGaDtJI11OFJJRGuNjDX4+NXT1LoPB3gv+lZAggZYDjzhbbdJ1pXtFFKZMYWM\nl5yjX30ersVldIY49kH7rSyxsjJZu8ynVBhvoCXwX84Rb13VHrVl0OlCm0Vr1219sNba1JF+Gowx\nZ/Inmv4mEGhlau0UthljlnnTBw0H3Awc7G23QtYVYa1dB6yD5PRBTUILpFXZa5Kml3DTKYvJQ7/U\nfRja6tr+++XDLuBZWdah4MrmX1ugsfjmQT+KUacS4p8WhVnUIzmocrNNYiFqNUkOA6fL8unAT7z1\n/2gcxwM7p9MnBAKB1qISk+SNwGuBpcaYTbjA9M8B3zfGvBN4DHizbL4BZ458EGeSfHtVV7MB4ghC\nyT0wLuqKgS1EuRAaXs5bpJMBMScO9cCtIqGMhfoP+yQDeJKhtD0p242lrKsEP/ahq+RWLcm0nYK1\n9rQSH52Qsq2lHg3bKtwLCXDMawC4Z67T/h+96RFYcZ9seK20o1RW+FUto2eX+Dyb+H8LReHNM2YN\nCcwYyeAlX2+sL3I3tdFPHPvQZgSPxkAgUEDrxT7oFWWc7vGwLhnFRydhWLptrctwWxbeKH7rpHml\n6XRjibSXEuVoLMCdYzvfAGARbyeqJ6AjxqoqvkOgvahIAfhpwOXhZPBdri0nNY4QT0vaxJNRCZJC\nIBAooPUkhazoJked09CcblE0bt8Oveo0JO1kHjZKMpTU6k6qkNSS4KUyM38EgEVc4/7t/zoMiohg\nLinYJrAvc6W3nDAxD30MrDxH5ZyRVD/RSWyKbEpx2+YRJIVAIFBAa9SSVOelYVIiyiRT0pqPwUZZ\npcaC1wM/lWV1MjlxurN9W9rnpfWNJWulXUFcOVQYesS1/RPELs8NjL0PzAJJE/elxGKAWLX8kV+f\nO302y6VuHyGWEJrltBSZVP8VXnSEW94r4slxD8mHn/D3aGrsQ3PIEr/ckZXQKxKrL/x/SruBuGL0\nMBWiwlGaDKif5dBbc5cUD3lJv/u/q2zREYAvS9tBeqBUoHVI+rukKKH9RC36eOjANUzpYsSraV6e\nRn3W9e1d2gnzpDBRVnsidTK+CJgry5X594TpQyAQKKC1JIUJYjNPOWch0S3Sh3hBUoXJ8K1lPtM+\ncikMukQqL+uVylAniWfLoIUBtVOKYrJgCqKl6K8nsA9hiZXZ6vmY6qnoKbWbMTMfpPit3f9f4CmZ\nFu9SCdiL/q2yAG2QFAKBQAGtJSlU6kKs0/o0pWJDkmPugQFJsnKrxH1t+I5rV2WJ+9Kke7TPWcT1\nHbT3TqtNcbW076vtUgMzwwCxjkDjIQpiJUQyvEXEg5NpjEv8YOL/AeLks8qTwK63ueUTvykrtbjx\nY1QrKbRWp+BT7ob6Ytl6aTUzr6GOOAVVat5AJBuedLmsWyFtF3HOmHIG64uBQ2S5XKBo6AzaBl/p\nCJD7LKzXEH95FkwDMqr4HYEeLm2qokkSfUtHlO1LyxxWX6IwTB8CgUABreGncKSxXEZt4pZKCr4p\nU+3IVduHVTl4FnElKckZM/wCALavWciiKOOcFo6d8i5AT95FPL0Q0U5iK9z270qc+2qC1NBmDAEd\n8px0fKzws3ryMfq+EYqf51Hl+4JSi1+RVkuTqTZ+Cs+fpiI/hSApBAKBAlpDp/AQtStl1L9ce9Us\ncEq1B1GpQCdwXyO6NUNy4C6X12HyycWwW+o+vFDMlYxRbKfqAA6VZfU2ecrb5jJZ1v3mVXvRgdmm\nHxcTAfFzGA2z64i9ZivJ+ZGCnyPYj59ILcasG+gzvLa2c9IqnUI96OxHpfY0g8C0GXoToh+XAeIh\npjc57wrSLp3bywPGvcAroxc6S9wZ6I9zGPAyWdbsHTdJmyEKw41MKW0WNRNwFD1TEnLPgaSnchLS\nnkkdO3xdpT7X087yz5pug4oJ04dAIFBA+0sKSQ9Ii6tZ5aOi3RAVKn+miLpmK/3mhMvqbHKGZ6b0\ngH73rcvqZ97HveICl5Wu/4W8Rj77ASBl6PijtFp0BmKl0T9VcrGBlkID6ZbAsDw7a9a5diRP9DB2\nyVRy4yfjXZPSgP//DKYBDJJCIBAooP0lBSXqST8Pg4vc4jJZdbxM0m61MCg1GwZKJVwBZ87ZLtuJ\nc8qQlK/4Y4ale3Q00K787wDxeOQwaXt4cWS61O1Vt/Aa4nIYR0v7H975k25sgdZHR3xVGM+BHtUT\nyfO4OotLdA5RRtj/vB6XgBiYlHgFVTNprMUMEySFQCBQwL4jKUR0wYCzFLBR3Ix/Llpg8yh0VPKV\n05KniE/5zh2Q12NIuwGY+Ae33P8j+exR4j53fwAsRwEwxkJ6eUw+U3OJpq3XAwbaC3Undr81I8Ab\n1fogJoSfd8BOWbe/PBvZcRiX5ygvuTjyKmG8Z1aqSu0DnYJWdFYTXyfcdiQA9mUvdKvk/pt7+2BC\nakfcKqGmJ+UACX6K8vJ9g/tFDDyKbbJuk2vGOqBHpg03ix06/znokSnKsEwR1nQT25Pc8Y3c7ufp\nBV4ln/nThiQa3HJ6mW0CrYH4swzvce2iBWyR+esynQ/0jMFBMlA9I8/O5HNgZYo6IVOPSYmV6f8S\n8TN0XtOuPEmYPgQCgQIqKRv3dZwP1VPW2mNk3aW4hFMTOH/Et1trd8hn5+PykE0B77fWbkw9cNVc\nQqSQ4eKUr6BiuIEpZxZ8LutG7yVGelszBR2ynTgg7SLPvGjfq6Qd56jI8UQSqERi3MVw8ycKTzlJ\n7HDSeYNrh3qhf3vi2pxf+nzmAntSvuNXEv/vStkm0JrIA7BGYg5+uYNlv3EOcPYgUTT25nky52Jk\nDuhz8dcdD3eDlWdtXOua6PMyhzi+ZuaoRFK4gThIU/kpcIy19licof18AGPM0cBbgD+Tfa41xpRL\nOhAIBFqMSmpJ/ocx5rDEutu8f38F/K0svwm4yVo7DjxijHkQeCXwX/Vfaqm6C7peIxyzcPJvAFiy\nWWMTJHIstxMyosQZd73zvJE8TErfqArK1ISrIp0MfiKe5uXFDGUuIjJJZUQH0XUGIOXsIzPV/4mv\nMdUvWwM4zkg5f6C1UVFRXqld+8NCcX2fJ5JAd5aDJp1+YTQrv3t2fxhTiVDzbvhVbWuMm6iDRiga\n3wF8T5aX4zoJZZOsmwFSfL+XS3GPwYWu7c5Alxh/14hC8NZHwIh4l0zrDsQFQvZzzQAwKCHQq/Ul\nvhFulQfASkfUAdws4dSnqDZZa/VeTJx6W6csEHcGavO+KOV6Aq3FFxP/yzNx8jj8wi3+/gWuwzjG\nGEyXG4Cy46J87BiHE92U49cb3L6vUE9IMnG2aI2LKJU9uoHU1SkYYy7Ezai/W8O+ZwJn1nP+QCDQ\neGruFIwxZ+AUkCfYOFPLZqKsJIDLYbaZFKy163DxpXExmIYjJsYB8QEY6oNxSW6iee66KNaYFKC9\n9t9Le7E3zTgo2upxGSEOOVmkjdu+Bp0yDYlSxl3q2uH9IHOHrNO0Wf8CHCfLKopqVOVbyl1gYFbR\n6Z8qwfV52QwZJ4HedrurWHTMiScyaZ2E2vPoM7Lds/ALJ0m+Ylwkyy4vNDKZjxEqqydRh39DTSZJ\nY8xJuKd4jbV21PtoGHiLMabbGHM4sBL4v7VfXiAQmGmmTcdmjLkReC3OZWsbbjJ8Ps55WwIJ+JW1\n9izZ/kKcnmESONdae8u0F9E0SaEMOkfrpjiVQUENCU3AolLBJHBkYh08K/PAJVlRas7dBHkxT2XE\n8az8PyYAAAbGSURBVGn8fNfmroTs62RP0Xec9ASxI5MKcJrirbLKPoFWQCuEdRLFPPxeiiTPmY8V\nU7f5o0RJdm2FUXkAreqoNDnL08AHZTtZlad8vmCNl0iXFCpKx9YaORpno1NQESxN+Z8n7hgGxYIw\noKHOc4heZDST7zhxfLa8yP/reegREVHdVqfka44bmJSTqZJzz1IYODZxkf8m7d8TaCVkGsgzOP8Z\nH1VMn4PL4AXxg7WEyA1acygO74A1+pYX+rPAP8SHHUkcCrx6qxQ+u1CqOFLI0RgIBKrnT1dS8FGp\nIYpzugbGEuHR/W6q8DMO4PWRHVmlA4i66o0iFcyZB33qtfi0a8Zz8SEnZFSYcBLIhqnlrDpZ1TMa\nXqt261GKM3C8m0Cr81lpfX2+esqqsno30TRDw6mj2OkHiYLz/GdUM0hPzXdt7v3FJoPUPI5BUggE\nAjXQIlGSK3AFMT8w3YbNIamUuXkJHCwVoXbIiH6Lu1Wvz03BGs3KrBJDJ6wXyaJTJIXsIuxSp5A0\nu2VUyMvIn98LRiQLiZA7emoBVnQUJtIqneBdlPqH+dJJoLU5v4JtLiKOg1FJQWNmvBB+/xm9WXRZ\n2WNca7+BC9UHJv24oNoIkkIgECigRSSFTcyalFCAuC/nDsHuL3HvGedsYp4T62vmSSKT0fp/lHVX\nAWJ2ysko3wXMkTnfhOgDcpp2K0fUH3e44x82OcGun8p2b9ARw0d/qr+T9hJKx4ME2odPTr9JklOk\nktgGKU6cmYyH93zqHlXRIp3CCpw9duYSSaQjYar5PHf3OYPvn6nu72kJWukdBaQziMyZ7yfygR+X\naceJT2K2SoZmKyK/FUVjNg8SQsv4Q3KMp5hnnanzGFFC/aHg2lTRGMyUAWHMKzOo+kt5o7VQ4bdr\nOGyYPgQCgQJaxST5NE7b8sx0284ASwnX4ROuo5B2vo5DrbX7TbdRS3QKAMaYOyuxoYbrCNcRrqO5\n1xGmD4FAoIDQKQQCgQJaqVNYN9sXIITrKCRcRyH7/HW0jE4hEAi0Bq0kKQQCgRagJToFY8xJxpj7\njTEPGmPKVX5t5DkPNsb8whhzjzHm/xljzpH1i40xPzXGPCDtoumO1aDryRpjfmuMWS//H26MuUPu\nyfeMMV3THaMB17DQGPNDY8x9xph7jTF/ORv3wxhznvwmfzDG3GiM6Zmp+2GM+box5iljzB+8dan3\nwDiukmu62xjzF02+jkvlt7nbGDNojFnofXa+XMf9xpgT6zn3rHcKUhfiGuBkXAnm06R+RLOZBD5o\nrT0aOB54r5z3o8Dt1tqVwO3y/0xwDnCv9/8lwOXW2iNxETJpeecbzZXArdbaFwF/Ltczo/fDGLMc\n5yL6cik+lMUlqZyp+3EDxVk7S92Dk3EpB1fikhBf1+TrmJl6K9baWf0D/hLY6P1/PnD+LFzHT4A3\nAPcDy2TdMuD+GTj3CtzD9jpcmleDc0zpSLtHTbqGBcAjiJ7JWz+j9wNXEuAJYDHOaXc9cOJM3g/g\nMOAP090DXO6109K2a8Z1JD4bAL4rywXvDLAR+MtazzvrkgLxQ6DMYK0IhxS7OQ64AzjAWqvZTbYS\nRTo1lStwiRg1nGUJsMNaDZqYkXtyOC4bzDdkGvNVY0wfM3w/rLWbgcuAx3FZZnYCdzHz98On1D2Y\nzWf3HYDmP23odbRCpzCrGGPmAj/CJZl93v/Mum63qeYZY4zW6byrmeepgA7gL4DrrLXH4dzOC6YK\nM3Q/FuEqjR2Oy4zbxzRJ+GeSmbgH01FPvZVKaIVOoeJaEY3GGNOJ6xC+a639sazeZoxZJp8vA54q\ntX+D+CtgjTHmUVyhh9fh5vYLjTEaxToT92QTsMlaKwUp+CGuk5jp+/F64BFr7dPW2hzwY9w9mun7\n4VPqHsz4s+vVW3mrdFANv45W6BR+DawU7XIXTmEyPM0+dWOMMbh0u/daa/3aX8PA6bJ8Ok7X0DSs\ntedba1dYaw/DffefW2vfiis6pjU6Z+I6tgJPGGOOklUnAPcww/cDN2043hgzR34jvY4ZvR8JSt2D\nYeAfxQpxPLDTm2Y0nBmrt9JMpVEVCpVVOG3qQ8CFM3TOV+PEwLuB38nfKtx8/nbgAeBnwOIZvA+v\nBdbL8gvkh30Q+AHQPQPnfylwp9yTIVxG0Rm/H7him/fhUkp8G5enbEbuB3AjTpeRw0lP7yx1D3AK\n4Wvkuf09zmLSzOt4EKc70Of1em/7C+U67gdOrufcwaMxEAgU0ArTh0Ag0EKETiEQCBQQOoVAIFBA\n6BQCgUABoVMIBAIFhE4hEAgUEDqFQCBQQOgUAoFAAf8fWidT1nvXudwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa679aeb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_tif_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.29430000e+04,   1.00000000e+00,   1.00000000e+00,\n",
       "          6.00000000e+00,   8.80000000e+01,   1.01000000e+03,\n",
       "          2.05700000e+03,   2.22700000e+03,   4.65000000e+02,\n",
       "          3.54000000e+02]),\n",
       " array([   0. ,   25.5,   51. ,   76.5,  102. ,  127.5,  153. ,  178.5,\n",
       "         204. ,  229.5,  255. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vD/gC8IfAO4HngX8e7XQWXpI3Al8HPlFVLw9vW2rHd5JeF83xXU7hMKOv6DjVVdXB9vMQ\n8B8M3nq+cPTtdvt5aHQzPCGm6m/JHfOqeqGqXquq3wL/xv+dWlgSvSZ5PYNfll+uqm+08pI8vpP1\nupiO73IKhyX/FR1Jfj/Jm44uA5cAjzPoc0sbtgW4azQzPGGm6m83cHW7q2Uj8NLQ6YlT0jHn1P+S\nwfGFQa9XJjkjyVpgHfDAyZ7ffCQJcDvwZFV9bmjTkju+U/W6qI7vqK/an8wHg7sbfsDgSv8nRz2f\nE9DfWxnc0fAIsP9oj8A5wD3A08B/AWePeq7z6PErDN5u/w+D865bp+qPwV0st7bj/RiwYdTzX4Be\nv9R6eZTBL4zzh8Z/svX6FHDZqOc/h37fx+CU0aPAw+1x+VI8vsfpddEcXz8hLUnqLKfTSpKkGTIc\nJEkdw0GS1DEcJEkdw0GS1DEcJEkdw0GS1DEcJEmd/wUHOw4jfvwTwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa678a81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(img_tif_adj.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([255, 255, 255], dtype=uint8)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_tif_adj.max((0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### Testing loader for DataFolder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from /home/data/CelebA/splits/train \n",
      "Found 162770 images in subfolders of: /home/data/CelebA/splits/train\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"/home/nbserver/BEGAN-pytorch/\")\n",
    "import data_loader as dl\n",
    "reload(dl)\n",
    "\n",
    "loader = dl.get_loader(\"/home/data/CelebA\", \"train\", 8, 128)\n",
    "loader = iter(loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 3, 128, 128])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff9cc981bd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FfAyuW4uUQfsm9mMBGpVS3wLw0wD+IYAHNGEAwHOE8OK273xPKfUDpdQP1tRt\nebTRRvvjt68NNCqlZgD+VwD/mfd+sVeJ6NWPCGi8998H8H0AeOfxuXc2EJCMSEex2jGDC0rISF7S\nhEqUrJjZaLJSUpKRqEQpQaWTVCf3bnBwXK9AYFRmouour0TcMmwYrGAUSrN0mBO8wLGHMww/EuDx\n3ieyY3RMrUX1dyAgq+vCz7NZL7GlVZilw8qyECyBRUWyLJMllsHECMSt0ZCHwIrMq9UGc2IVbj3F\ntVZhtaU289yslsVcmgEDeUS95Wo8hYZSi20fzr8jb2m5avGatBOqJyH2fnj/HmYkC8fA69HBFDXV\nW5S0CnKVZ6aVAKr8u07zAifkWbAYinUKRR1W30dv07XQmHh9vRBv8eJ6Rb+BwgETpMgL7AcnY4BX\n6Cuqc3hlLoQcxSlDay0MbV/rcP7WEzvyYiFSe8sq3I96aqQbFK/sZ6enKCj1uyR5uGtKCR9MJjAk\nH/eciF7a5NJPQiQId8sk6frYjdACeL+pfa1JQSmVI0wIf9t7/7/R2y+UUo+898+UUo8AvHyzvYVS\nY9YKlHZcPYcM0UWyEgI4WBu/DwQXU9SPElUjgBIYIrHN+XOfMBMh+ygklOB8P2c0eihwQxZ2dXOI\nUAvtIzNZZEGKaAplIbwT7UJhtg0deippPqLByj0DJ1UlA7LZMgLeYkHMPR4c1jpByBlMZO3D5aaB\npYd2SSCX1zlyKp2+XnGmJBNUuxcRkljs1RJQxnLkLeIEx1LwLbmruTHoB1Jppu03zy9Rl2u5LgBY\nNB5FHs7J8G+XtMTj3zGnCaNWHod1AAJnNDlkWS73oSJ68+n9AM5WB6exOKplcNhjRffBmNh4RjvS\ndyTuAHeVLooqStlTdsZ7D5Y8ekAPtqPQJS9nEm5cLQMoW04aTEkJmjnNWin0DXEMqJDM8PcWc6wv\nqLs3FYW5TMviyPIC2mdRmYlfPT8Deofl+yb2lcMHFVyCvwXgh977/y756NcB/Dz9/fMAfu2rHmO0\n0Ub7ydvX8RT+NQB/GcDvKKV+m977awD+OoC/q5T6BQAfA/i5L96VgtYmAHyykodPwioM5HlsIOoc\n5+J9DCWESaiQafY2eP+M9A07WvnyCacdWQMSGTSFDWUZZnb2GJxvkRfEDBwiU1JxcRS5vVVRwzDv\nQaZeYiAOjciN9cSOs8pBUTrRRHFGAMCLz57jnAp5lOI2bE4atNbkYiqVCbh2eXlJ5xibm3LD1S31\nIdhuOigwTX/bAAAgAElEQVS6rp5c/nboBdRi0qUkggcHQ/dokPtohQvB5ysyZzaEbACg6XvtMGC9\npQYqWTifq2UjQjHcp6Iw3EotqheXxNI0QxNCAgDlZVi1c1MIC7UkcLAouPlsJiFCTqnOzLqdtnUA\nsFothJnIjWY35NIDXrxW9khMZkRr8SNihvbkZT168AgDbV9PqPv0x89xchQ8m3t0zO12JTUdOStf\n032c1hUcjaGGdSGHWCMxDFGjMVJ4GIRktqsVbc43ta+Tffi/cVN1gO1nvup+RxtttD9euxOMxiBa\napCbUjwFqXDUDMw4IQulHXfivMTA4a5AajAGF5WQouzA+1dg1UueefvewmTc+oy2Y+Ary6UE2uTc\nHciDoBCYjFeaiaSH+BoyIrG4IZM6hJybmyqHmlbJnFYCxiSOjg5hqV7Bk5CodQqXtFpuPnsh93FJ\npdNDH47JAild12OxCN4Jp8UG32FyQC3gqUdBO3SxqSo7ACIco1CQF6BaRfeqE7A3o6q9gQBYC0B5\nLgdNGXbs3fF991haqhPgateam7IWwoB0Pf0GPoPmku9tGBOZttFToGsuy3AdB9MDbKhbE7NFy6KA\nof2uiBx1dHwacQvyvrZUGblYXGOxSrAE2ldOBK8T8uSuLoOndr1aS3+LviEZuUKjvVzReQfs5617\n90QYRxxaSr0reBSGPQXG0QYYEeOh+gn4CJJnsfyft2epwDe1sfZhtNFG27E74ilolEWN6XR6s+aA\nMw22B6/8WnNGwgq9WV51bPN+o0+E18kqBfmsI9ScG4LmphdPoSfEnvkinCUJ583HNMgy9jw4lVZK\nOkORp5LRbG4KgHs4drzKwsk1MO2a+xEM3SBkq4HO4/p6gadE4+V+jRWh+QCwJVrsYsn1Di0GWpEO\nSfdgvu7BRRicvq2qEjOi6oJXec9ZkEH2y+nTPguyYQDQs/Q5YzSZFkwhdsuKKTL+fXIdBfI7qsL0\nQ9RCMKIzkMX98yrJ93jw0HR9G/IUMlqN56tNzGDQPiZ1hQk34aUf99XFpWR5plOSjKPrPD45FBn1\njiojgzQf6zRM6X4Hb6Kqa5GF4yrZpnfoSa6+pDHRbxs8uhfwhSInoV72MLxFIRkX8sLaRjxmxrm0\n0pIyZ3yCzVqLLRHf3tTuyKSgYIxBUVTSvJPpf5xSa5qtMBr5h9Baxzx/lj6gRvYLROGLLBvkM+n0\nO/Sx+EpoBSrhKexu7+wgnAhRdfYK2KvByE0l/AgOG/KcQbcengqXup56A/S9FBLdOwuqP5y+Wi1X\nWFEx05KArA8/+hQffvghAGBDg6+sa3mAFINhXL8wOBRStkvg4gABKweapHSuxXXmSSEjtmhdWdQ0\n6GoahF3fSN5823IRFrnDWmGQsvQYPnTtbn8G33nRhWTz9FlnHbA34G2upVZCIkXn4Ol31JQeztnN\nz/P4sHDz2WYjTWG5vmVaH2JLacrtNtz7yYRVnwaZDLhwraxKWUgY33vwMHD1Xl9eh8YtAK4oPZzB\n44A1MSmkvJovYWjgnZ4e0n1jjoGXQcmcB2M1upZT4uGzoigjWEmvqejQfk+SL7IxfBhttNF27E54\nClorVFWNyWSKA2przisuu6uAl5lavAKVJV6Dkc94dRTGn2NPIa4Y3BodiKupAIgml/JbIS9l7LpG\nl5Fn49C0h0BEbkxa5ELE4lmewUg/bNG3VBVIHsjB8QEeU7luUbALyKtrhtevAwHm6SVLmb1GR+Bj\nQ+HPfPVaCC3cr4AVn7dNK9fH5K+yrJFxY1kw1VzD91ytye9Qmth6ScFxHUXTbLGmtN1qzQ1dezl9\nBrmM1CoYEWrZULv3YbDo6TsV97Cgcx0GJ7UPzNzsOgVLwG9OtRJwUcmaiU85C8xYi64L2xVMcCqK\n2LiWwoztepBxwee92oR9HZ8cCsCHjkIWraUnBhObbAJCzufhN+voNzYKUntjaXA2fY8t3dM5KU1X\n5J3AKBnDPE6mupZUMZPLgOjtcDqePcYs01JT86Y2egqjjTbajt0RT8FgNj3FpDxCkQXAhrUNDAKf\nPVM1soz7G7AS8ioRXeUY2qCiCjfWJ2D5sSzzKPKwP08dicrCyeyamym9d4CiCCCfAvccCFO2yUrM\nZuyJsPDKNlZJglYds0I+Dau0pttsSfPf2AwPDsJn9b0Qu9a1hylJNJSAoYt5+N6zF1t89Czs9+OL\nUHn3+uoSK6bbOq42zDGjxqusM1DQyluWlawmXHNwPJvJPTo0MSbN90hU4gVZi4G0JJhOvW22aBvi\n7i+pYpG0CJq2g6HOVowZ5HmOvg/br4lM1bQdtgQODoRjbMlbgTHICAtpaRvbrWEIvNOWvBOfpN6y\nXRXl+Xorqx8DjXVeImfsiTUI9ApmIJCXHBARg90M0oKesaGtdcg099II18KkuMFalPTdrglAcN/1\neL0NnzeEF5W5wfFhwJCekHjuEWFgR5MCE/KEcpb061tMCW+5ojSubhymLHBD4CnT1/OqhM6+3GN+\nJyaFADQWyE2BIo9CJwCQUc4bygOMeHdcZ6CFZcaAILyGHRgx5HxuBAZZgKUqWYPRSs2DPAwmR8Hu\nroQikUvODVacaCRaKX7SUpDUYK3DYHBULFNSQr/MC0xrPidmHLaoZ1SYQzrhL1+GnPezFytcESeh\nIVBPKYWCGHlDFwGzklh8DNgekRpRXUZNR37NlEZGbnhRsjt+26QQZfeZX8EApXNWwgUuQWa3ebFY\nohs48xIFQZhlecihyGDBc8BiHSabaxKT2XQDBgoHKgL4nCrRU9EYl4E7OLkWJRQ+L/+KoheFWn3X\nI+N0AosWlQV0Q8VfW9Z5pMXJGCmjns1m8hmPAQbE2bz3aGjSa6l5bp7nsnhw2HFwcIgVhVErrhZm\nFWo1xazkxYnGFzRmNNkMLrYM4EwKvxYVTwq13Jc3tTF8GG200XbsTngKYiquyDK7UYOW3Oaizjsl\nF1kpLTM0f68sS+G883uS4x9s0tA1rvyc62ZwxhizA9TIySGsllHSLcqrSTNYzqkXpQiFeEr3OXKN\nHTw68nDKPDIymXHIlXzXc1YZ/hQvXgVP4WLzmo4JYdMVtLJPJxNpjMpu74yk0U6PThKREE5zGZQM\nNBZ8X3RMH0rYQAxFa0XezUuqMXoSzH84oMrFzbZBT6Ebg4R930mlJV9n2w+wnNLNlnSvWIRmhYaA\nTAYfTaalmwqnk4MaOHldnJpM2ItOyt35bgfgNHw3ttrj5B2v8lz7UJalHItBVmOM3Oe0RJ3/z/dR\nEdC8Wa/Fs2VOyQcXH+KUyr89e71qRvcvwzUNonvHYZuyyrDcBA/0+DQI12QmRzUJAH1N5eOGPG2l\nDRo63ze10VMYbbTRduxueArE0XA2rsLS0p020ZmGIQEL7vJjTCEzdLra77O62Jvo+17ScZHc4eVv\njh+LohCWG3sKcUUadrjvQPAOOL3F72WZlspAJqDwDKy1imKavCgDWC5DHPvBh58AAH7vh0G198OP\nX4JK7uEMtwPTIvDBceR0MkVJseqUqgEPCHA8PDyU1SnPIuGLPSau2Qgt8wiTEd2KiKGw1yOK1kmr\nP77vfB+PhnivGOztu1ZESFe0Cm+2W3TDLsFGPC6tpLHslnAE541gSBX1lfBNI2IvA2FOjC30nY1d\nt5iMZgywR+pRGuJNMUuzY3KXdWjpGvgemzzHnMBexqC4RqbteyGV5dRotigK8ZK4h0RuMgz0HoOF\nh0RiW21bbKnegoGvx/fPMaU2gUPGJKoJavq9ORXtCTvrB4uBcJQ3tbsxKVBBFKCSh4+oqooLkTLk\nKtw06c5rrUwK/L3AcuTPuRAKtA+DsuR8Lk0KOlKTOTwpiyJpKLPblCbVihT3UGnsg5q5ykQsgyOQ\nTPofemSGQbGwfdt6fPwkKCv/9j/9XQDA5TwMlqIssCEBFk2ciyI3Mhkc0yA5OTqWJi2nJ4E6e0Lh\nxNFBnBRY8dl7L6EB07QDmEgTsvTTjHluUa0WKf5BgEaeFASA7Xt5oC2XQpcFplQCXRbEHTBR5dgI\nj4ALojwsPewXVGy06Vt4swsEl3kuDzLrMXKz5dWmkd+KS7LLPJfSdqGjey/XkAlin6hakfFUopSS\ncIAXsSjPrzGlCXm9CaHfdtMI05TLqrtOoyJXn1WwOBx799FDEYyZk/5kXde49yAUX/U9A5gVJpMQ\nNkzomFDcRbwXRuqb2hg+jDbaaDt2JzwFhbByO+cSURDmlHOqEaKwzAVDClre49Se1lq8Bk5N8qtC\nJhqQUccxXfEJTPTuhgfC/+/7XoAml0hgMTDJaSsFD8O1DxQi8FpjMic5b2YIfvjRE/yzfxZ6DCwX\nJBNGqs7N0IsrPCXXcjqbSrqRQ4WqirUj3GaOPYWD2UHikif6lJbvR7yW/fBBGu0ohb4Xtyu8egNL\nvBFeNcXbyzJpW8cro3cWOXH7vSXPz1qRYSvzPQ5IV8KeBk/IcxgzX6PjehVuu6fjiqsIVOzICzLG\nSxERpxq7rpNWbOw1Gh3HgNm7V8YY+ZstSgXupqd5ewZcz86C9/bhhx+J9N+WJdisghuoVR2dj6f7\n/qq8gj8O1z6jsvpl08EQ87GcUVftrIAhuUAJj0hOUCFDtx2BxtFGG+1r2J3wFKAUlNY76TCJ4X2M\nE2Mrev5akgrM8xvv8fYRp4jxsnw2eEld6aQVfRRmYckrbsraiKfA0l0e0Xvhng1GWykvPppRfwaS\nYJsd1Gg2Ybb/jNql/d7/84e4uibQqgzx4ZLRRa2klDej+1MXpXglXE15cnSEc2r2ekKKw8xsrKoq\nxsVy7YNUo6YNevc9J8ZwACRq27wrd0MYlI/jskzidkuroBt6DD0DsAQIKsh2W66HIHDOTivpKLWd\nhNWyGywWawIfyQPwSkv/Dmd2JePyXEnMn3p8glsxgSszcJ6UknvGqKJq9b73AHjZr3ie7BlZJ2Kx\nmw2n2Y2kP5lpmptcGuNWBGCysvXl9VI8LJZxy4sShyfh/jKJb7ttMJtxXUg4/0nO6tgmisW+oY2e\nwmijjbZjd8JTUEqhKEoUJo+rDMdotI3O1M6sDQQxUKEwR+VKqRRM9w+EFUzi5eQz9jIYnS8KI7Gq\npAxpRc2MgmJ1ds/pPCNpTV45ysKgpBVrRi3le8NxZ4sV9XH49GmoZVhuGhwQB/6zl0Hjf0t18w5e\n5Onqilb+skJdckPX4FmcnZ6JFgPLxDPtOfXCmBjklRIXIWrFuhuxM1z0rrhCVCjEzsuXeSWVe6y1\ndD1y/JnNYfd6O+S5ETERrnAUsZosQ0c9LJopeQKI+hXDnKjSfS9eD9cysCyB95CakAn9Ftv1SijS\ngh/pSDjSe9c0WCcVlNEr0NH7knsb/msHKzoTA3X1qusJepFVo94Yyzk67q/hwnbPqcfmydGRnA9X\nX5qixPQwjIHZlMRuJxOsKHXJArUDHXs6mX3plf9OTApaaVRlBa2UuPUysDgliJi+8z6CTD4miMK/\nSkVlH7obecFNXlQSPoTPMm2S8mgln7kkBcn7BQKQOAwxHKErEIEWVnEaAPTUKFZ75tEzK7KTVmFP\nnoSWa33n0ZFKdUMalHMBlCopf2Vw8XA6wzGVmXPI8ODefUlFcmjB5py70clbKQXNLjGGnW1TS4O2\n1HUGgr4MT8j7YQeyLHI6HJdfZwHRAzDw5JDnwsrk8KSiEK3Ic1h6qJgjobK1lI2T9gy6po/sSW7g\nQ4VuXimZWPj0p9MaNelAcuOcrrOx3ZooUkc2LIeNzJgtijyKrCi+PzzWIqh9QL/ZMFj5nFm5UJno\nKwrnhpvgDgNXaaMmYZznF1eS1vypd0O37812I+e0XoX0Z84FgtMpvBlTkqONNtrXsDvhKSilkBkD\nlbDp2BuQltoJxz6mAuMaFrtBIWEtyhEA4NZqMa1z0U7k1c85K0SY6CkwYzITF5TVn4few2JXdizP\nNKZEKKknBKI1gXzTtBt89ix4CJrIN/3QQdEKt96w7BdVvk1rHB2Ty0jVb0eHhzij3gH3zwOZ5fjo\nSEKgbA8QTNv57XgCAujG9+JtYxeeNvU+kfZij0uJduG+6+188vtxx6JMQend7TIdqzWZBcq9Dbz3\n0mau67g7lkVDsmkdNdB1g8NyQ4Qgcs1NQUzCssCGxtOWQoaut8jJY6mojD4v6h3tzvQ1Nb5Tg/NR\n8CdhifK9cMJojfvkytaSO0p5JWIpIkST83lXsBQeL6mNnbM9WiJzVSYSyHgMM+GMm/dWZYmqHoHG\n0UYb7WvYj6PBbAbgBwCeeu//vFLqfQC/CuAMwG8B+Mve++7z9uERPATlfaSqchUeH8e7uEb53dUK\nu1sizuW8IsVYV+3nz7xCXAzS1eF2scsgHUfNRImI0ulB/mZ67KzKcO88eAqX82d0/LDNq1fPsSVV\nX07BWafw4mXAGToKJHsSbKldJbRooTYn6UemNk8nEyEo7V4RdsBDAa+sFa9B+cTTUbv3bXeHu6la\npbTgOjGFyUujQlws6TfwKumxSV6BgpCc9lupN3krtQYV11TMaulCJYJ1poBHiKcvl9RHk7yCQtWY\nUVquIHxpvrzCmjQbeJXPi0ls2rrn9aRjJwUa2Wxsaiqf8facJjQmF6Dxahu8xm3b4eAoEMwGkYfj\nRrOL0LUKQEXeQ1nmyHQYAx9/8lE47zyXe0PDROjiV5ev8Nbb7+LL2I8jfPgrAH4IgKRo8TcA/Pfe\n+19VSv2PAH4BwN/83D14cmmdEz6AiJbwANI6cTvpa8lzmz74uxNEGkbEL6TsRVYtTiebG26jKDPr\nWN9ACjxloRD5isSxVz0uXwcU+XoeXvMyXNPrywu05ApfkkpRWZ1jSaq/4GIfallXlAUOqQT6wf37\nAIBHDx/i/r3wN5cs53key6MZMEuu4zaXOHYuvuk0qr2/0u+lE8CNiZb3rTJ41rHkicP7KFbM5wUv\nv3dO+6ochWh2kJ+NXfO6qkSMp+TXcitanNuWpN5bKnXebgHiRhgqET85PpDy6PWaBF0266S0fvc1\nnRTSwjkGH/dbyhVFIUAjC+McHBxJS7ujkzChN32HaxLQWXLtA4d0tofNmGFJWZy2R8mq4EMYLy9e\nfCasycvLUFp/TOBm02ywoWY3b2pfK3xQSr0N4N8F8Mv0fwXg3wTw92iTXwHwF77OMUYbbbSfrH1d\nT+F/APBfAKDSLJwBuPbec37rCYC33mRH3nk4mwJ8rMzLngIkdQPcEj6kOvdq93PxZhHcVyCWuGY6\nT7wMI/tyjkumibNPQFWWG1E3ZrH/Ii8lrckg59RvcXlBrcHeehsA8PGTPwIAXF4tcTUPszfnqOcX\nF1F8xIYV4/4srCYmy0Tl+vHb74XPHj7EIbmdDKg5uo98H1Jz3kd+AuI2kkZkxWaXhE6y/U27BX9L\n/TDaxku4wU1ivYP0Q9DJ/lmmjD2dnEC32exAFJ65PHhTL1EWu2Xa1WQr6T6+zpeXtAI3jcjCtUyh\nMBqaUn+Hx1SRue6k7FpSky4NCzilzH0reqmsndF5MOradx02lOosSWdxs91iTbUXE248VMZ0s9sD\n0lerRdQZpdAiN0b2oXNH13mN0xehZf1DAp2vroMHeu/sHJeXL/Fl7Ct7CkqpPw/gpff+t77i97+n\nlPqBUuoHV/PFVz2N0UYb7cdsX7cV/b+nlPp3AFQImMIvAThWShnyFt4G8PS2L3vvvw/g+wDwL3zn\n2z4oBfcYqHNSFC0hMMobGMcVfbQSKSsreGTCxVZzaVQMENVeceqIhVgGMA6aGRbCjJ4Cew+Wu6iq\nTNKIkhpVFoaISQVVq5lhhYIEQBo6n+U2HHuxKfD0Fashh/OeL6/QE1WyqFhdOnz/vUfv4U++/2cA\nAGfvvh8+m85gS+42xGieR8b1GJJ25JXOyXYx++h3QUEQ+YvBROEpMVMwehsREFDSgNYlzEcAUM6i\n8JLj4yMmXgYzfnJ4Yn+yNoOW+D2TKkb+XdWsgsmD51QUdN8Lj0lJsmYuLDJtE+LrTHm8vCaC0kDX\nWc0As9tjYlZ1cDl37iJhHhaoBeDZoxRmbZRmYyGTgkhGRuWi9dATKa1rGiEozRcEIFYlHj16FL5L\nwr0MTLZrJ7J91rAHlQlWAk1ScI3BB09D/cThUfAuP3setDkOZrUI6LypfWVPwXv/X3rv3/befwvA\nXwLwf3rv/wMA/xeAv0ib/TyAX/uqxxhttNF+8vZNkJf+KoBfVUr9NwD+MYC/9UVf8N6j73sMthcP\ngVer2Bcy5cNHufBYwRfjZMYlePWL22iRyuZVPsuUKPVwmmuwTmLzlMoMhFr3gSjBHI+3bSsrAPPj\nfb+RGJtXv8urMJu/fPnqhtDntmklrp6QGOjjx48BAN/97nfx/vvfAgBMCUfIiyISZXhldw4xHUu3\nQFZvF9OJQhBzsoJz9SXdxZ17upP4layDvHPjM/m/VslvwLtUkn2QprOIJKBIYZcriMQgSstVqpRu\nTZGyrSW+X1MHqhW1qX/xeoHWUvXiIiD26+0aGcXkNSH1cF4k7gxlCbjmoBsGNEljWT5v1kcQmjjV\nt5jMiNYHYzj79HF+j8fAAWWYFuRFHB8f43p+zXdTvrOhlCXXORjt5TvPqOr24dkx7WspGgtvaj+W\nScF7/5sAfpP+/gDAn/tS34dHP3SwttsDdlLWWxb/NlHyyssAY4AoFvR4YTwmKcq9lN1gPfbz7Klb\nHQc/DQSrkHG3ZG4ZppRsyArLJ7MTrGj0f/zpEwDAxevgzm6bRlSCr67Dj+6VlqYdrMj8ne98N7x+\n90/g7DwUOqmCaxrSOo7kdT8V+TkpycBQvGnxru1NDkoJUMu3ykMnwO5eM1knmVxpqOq9h+fQTMIT\nl0wKDJpGvgo36ylJRMVqD03742a4SmkpR+561lek3zXLRd3YqwDA2es1Wmrg0lHGLisMogQ3p79Z\nEVqjKKmlHF+edVHAh9OmLNKiM7n1to8CQGxpef+KuoazgE4szCukczWzOVNpAP6er0uAekt8QmOt\noBMq8ww3Cty+wEZG42ijjbZjd6L2Ad7D2k5CByDty5CuZfupyLiiO2HaRTKN5+k74fXz7D3YuKIy\ncJTtSbWF7eN+9/fPHoiBlhWG25JtN3MsaSZ/8SKkhLhZ7mAd5pRxYdexmkxRU03F2++8AwD49re/\nDQA4O7+XlIjvCsjQm+FFq4gr/hjsdnhK3fhUqf21hVZorePfUiDhZCnyLPDilWyX7bm6TsUxwCw9\nk1kY8hQYhCzLEhMWRqF7xbUEZVlj8iJ4aexF9P2ABTEat+vwW6i+lFDF74+JMhfP0HBT29xHQFfE\nX+m/zgowekSen7VWCFMcPqYR15KUoc/PA1jYtq2Q1parKPfGadirq3DeGl6Uzq/mYR8XV3O6FwqH\nsyN8GRs9hdFGG23H7oSn4L2HtT0JfOyCg6k4KgOIpMsJpSItOnZtuim5tlsDQektllv3SkhRccVL\nvZPdqFurLFakeU6DahHjgGAVDltaFV68DMSSVxdhtXr58qV8VlAzXKUNTqjq8f33g4fw6FEAGquq\niufG7dUTzEo+8/H4AiomwKB4OKmnI8Sumx6AkJCYQAYv8bTmNKVyCQaxq7UQsIY9784l3gOSc5S+\n9/RbMZkKAMu2aSGo5bG3ongPDob6jvpD8iJIo6GuJ6I30JLEnXcWOaW7mUi2adooOsO0Yi4RdVbI\nZabkRrNGZOTynLGHtA8ne6ORFh27loVz7bo2epAESLN3UhSFEJtyIqjN53PBNqJmhkdP92/bhNdX\nFwHULk2OV6+v8GXsbkwKCJNCGADMxQ+fMZPLugGKeApcfATcnBQAH8twhbfOA8dAa+YY8D60uLEc\nglg7JCIv+6zIiNhzuKN1LsrA6UO1JET4gmogGip+Wi5XsTEuldCWVYV7VMvAWQfOQngPGQi9v+3h\njW/tC6kIW9Pr+FyKS5+ERbKTJJuwl2HwUFCKH2Q+po7l1Gr3nLxzAkKKGI726c0EX6CVOhWe/OI1\nqYRXAYQCJgXOSnHoFGfJkhqvKu4mnpdyDSzEorSC4WsYuFlQg16UwHcBWwVAU4s/Bg7t0CdZMuJV\nFFwXYyS0qetCzm1f8bosS2lYy2Aij72qqmSMsTJ00zSyAPnDcMxms4lgLE1YGTFsT8/vI8tj3443\nsTF8GG200XbsTngKAIIn65wsIgL4+FTTjusRaBsd01q7VZLBiiJdKYLiMq9E7G14lcuyx+7bMKjo\nKexLkzkHSysLqwcbBVhKCXEab7te4ZNPA5mT3e8LUu31iHUchs5tOp3hvffeAwDcp0rIikKLAHAR\n8JVU7YkgTaJWrfYdiSSEktJftRsW7N83v7eSpx3upN1eyiTY86oE2NVKPL/ocdmYt7+lWjOJZ8L7\niF5JbFWXAbQysjSZcVE/UiodE6m0UxKp6R6Fe5tlChPuVEWuf+9e4XpBrvxeNtsOgxxLszK198Kr\n4NCi63iVL0RFOe0ixQ1pGSzcbjcCNue0X656VUphQ3pzeZKu5mNx7wiltSjh5DRmNsSw/PCTJ9LA\n9k1t9BRGG220HbsTnoKCh1IeWRbZilw/zjNx17UCPhYE5gzWwnYMzjBXPvaBNCbGd+H/uYBh3DsR\nysBxByTxOmJcpyWepljdDpiSBJhOlmXlONUVPIar67nw26+vQ3qIG6r2/SAHYwbk4dERzslDmNJK\nYXIGtJKGuSpiLlHplwk0KlY78uapIE3UTaN7G//mmhDlEYN4cR6YrZOAuPKhw056NPlMKR37SYis\nXqyt4OOE35++K94PI5rxHLm+Rbkifi5iL076jt6s4FRyDafUP0ErhQmt1tzb0mWl9PO8pNQe39vB\nhea44W9BuhNvlLAqzx3ObOxG1XKqO5NaidiJKsO9e/d4dwBiPUVd10JkY7xhGAYZF17k8jK8eh5w\nqzVpMkwId5hfzmHfeaNCZbE7MSlAcaMNLw88P9CxfZvDvuy2dcBAbjuDhWl7L94Hb5/nRnLYQn3W\neaLuMokAACAASURBVATeZJC6pEUdPUAs4LFtkTE7j0DOTCs5FreBWywWWJNoCjd8YZWlfrAwewjy\n8cmJZB8EVeZCICQMuERD8Db9QLmlKcAYvgCeKjgjkYYMMTOBxOXnz7hASsPR75Hwlm8wH2VfPv4u\nYCDQA4pEbYRa7V3cA//B1ekJKxJJtkJCEBvPR4MBZtZNZJapl2thuX2F2ICG1Yp8OYmhwYcfAwAW\nK1LG6h06DjkdafwnzYy5qj9h0EDRQlLPaNJJm/fSdTZNi+dUvMRjnxfGECKG9xhQz/McA4WLOY0h\nbNai/cgTbtuH18NZjcVqgy9jY/gw2mij7did8BS00ijLEt7b6CnQjM09G7zLkBtqyUarcuYVNOnV\ncfiQ5/kNj4Jn3izLxFNg8yrm6nm211lSLRFrr+g8BlzPA9+gJtf/6GAmHsuG245v19gQF+Ga6hs4\nHw1AGsFOqRhnNpthRn8zc25HUm1vhflRTsKN1nrxkx/xBX7Z53bEv9MQxIsrH48g90oOxvtKQwu+\nkT7WSkj6WUXVbAbzuJhIx32w3Jp3SprjWBXvi3godCgmR4YzDKs75/29T66ZhUzKGhtSSl6RhJl1\nwS0fVi2sMGXJg1MKPTEkfR/DGCB4mMyrWC7DmCiKMhmTu+MwXF/k2gDAZrPBakVeQRFBSG4yU5C0\n3IMHD1CTdN/LZy9pe0rLZjnW5KG+qY2ewmijjbZjd8JTUFrRDO5EtZjBQU2iFVoV0CrEg3FGjeq8\nQljKIESOWMsQl1e3X8ugEu1nXmgySDtwLpPlZrF1XaKntNOMGsdOJiUuLghUfB3Yi4v5HJdJVSQA\nbGjGLuspJtT4dUaklMl0KnJjnEJNW6dFkg63ZvORqJR0sZI1ndvuuVhPIgxCF+9BzD5GHGC//Jp5\nXio5p5iuTOpKsGvhfHYZofDpdgkGse8CpexLxiX5d1SZ1IJkApBaZPzbklCP5wp652WFJkchpC13\nHQu0Zov79wKwd3UdqlK3lP5rh2s4UtnueL/Wx6rKxBMCAqPVEvZQSDu9RtKTDEwWRS7pSU6lNiTj\n1ratPAdUDb7T4n5L4GNuKmgV9reheo6C3N7D2Qyw3Ir+Fd7ERk9htNFG27G74SkohSwzMLkSbEDv\nVamZrIB3POMS/99Hufe4vRJRVr1XvWedS7pMkaegByhGezXTaJUInjBWYJmWrIEpI9hUhDGfX2K1\nCmnHjmr0X1++xlNqHsurQ8OpptmRpCK5p0GWmZ3uQum1p/FnWqOw33dzGAZ4lg8jVD72c0i6O8nq\n7pP0IadeIWnNfY8hbiH/oQzDTTwi/N9Jik7MpZWtVs5REpxCM2axHQ8tY4Jxhlw8CUXAQQYNTcfy\n4CbFVDOR6X1/Bc55kWDnVbv2Vjpx3b8fKhVX2/Cbtb2HvQrpvoGp9Urd8I5izU5Mp4Pk2EKD3ljV\nGbZzUjkZK4J57A1J6iW8brdbvPNu6OMwIf2F5XIrXiM3Fn76SdBV6NsO905YV/nN7E5MCgH1GWBU\nHvPait120KvjVLqUtVqfyQDXkmbT2HeAIqA03NQRRCbPiAi1WBvDBwKQuG5BuQGFDoCgpYd8MZ+j\nIQ7CltKQzy+u8fIqgFVr0tQbPA9qLWmltLyAQyc5DxUbpLJ1NEjtYNH3rP3HBTUdMp+45IBoNioF\naB5gokzlAZoQLfiBirUdIprCdRRKyRlLCOJcoomJnWPDOcAxmy9uP1DtAJ+/t04mGdZE5NSuHazc\nFy5qUqaQ7bmJTF3kKEjgRqSdeMEojEySmtN5ysBIgV14nViNcxI4aYjPsD0Ov/VmscGG+kN0fWRd\nDqxcZTiHSvdTxfCFT7auK5ngeaFYrdaAFHfxYhC7dw/EnuUxbK3HBXWlfqcOKuEPz0+xWoax9s7D\nBwCAGdVAtJsVzs9GRuNoo432NexueArwUL4PK7ZjQI3JN7y6DVBqlzgTKCzsQsf0HUt18dWxwIfz\nLlbr8WJmI9OPXTBnbdRoJE+BJfgm9QwlpUYzZv5NPRaXIe34+mUAFz96+hILAqao7ykUiX7UswNo\nclm5N0DXt8io8w/3ceC0UmcHbMgD6RcEfDUNNuvAums41el65FyZx/0QSgLYciNhkqNeFgoeGR9D\nERjlndR7sHPMtxNaCcOT0cfUq4phAeL3WRWZWrg1TSudkJaLpbzHLD7+bvToonsvtSBVAUPnXdA9\nPZhNMSGmqeEOSgS+aR0YiUCUUvO5Qkaoo6aal1mXibfRcQ3BLNQqLGY1rgnUbrZ0XwBg2FW1kdoQ\nnUmOmzVA+74Xj4mBz7Isb3Sj2nAzWWdFVZxJfHWVoacOWA2lOh8cH+Ps4AHkhgGYfSewGC9fvMA7\njx7gy9joKYw22mg7dkc8BQAIVX+K+ysk0h3hvxZecbcmiq/2vh9eFPbFVRhvtM5L/MsrkXVWRFN4\n9lYJjTYSn2jG7jus17QPWgXruhKCEotpLpcr0f1vqJrt6CSAQNPpgax6PRFR5vO5rJb7Qpur5Qqf\nfvpJ2O+LIJixuLoSpV9ehY1W4ilM67AKHlAb9+PDGQ4PQ91+RStvWRTQrIrMnaW8lxqDeGe55iDG\ntioBFRm87ShvxuSart1iswwALKsNb7cNVkS7XZD82LZp5fhCUeaKR51J09ycvJ+8LFER+Ws2ndF9\nbLHZhM9PTsN9PqK0r7W9/MaQqttIm2eh1CxR8a7o/p2ehh6NDzuLi2VYwRf0e7rewdK62pH3xUBp\nSiFnARYe4+GcqONYlt0AlGfknTjnhBrP+zs8PBCQmvd1Nb/Ge+++F86NxkRF+Es9mYjn8aZ2JyYF\nD2BwDvBaXFH28zQh/IEhtjtYrVcJi46BHiU5bDb+mrWxZDrKbSvRchx6nnQcDIE9Bakn1xWBWMOA\nplnTiRPPvGmErcivXdcTiARMSUTj5DjUNsyv5zgglJiZcwfdIWpSI+YJkXkOz58/x2ek0rum8uv1\nYokFPXBdEyeFkiavOc0r3Iz06OgA59Sdekao9dHBAWY+nIefcGMZvTOgwx2C3BcOLSQMsw6WisCa\nTXjIWWtwtVrg+vJi5z3nYsjHsuh55kXcRABjdqmVkxaCQ8fZIS2/HxcKdW0DOgRaYiX2J+Gz6XSC\njIFJKcn38HtCLXqwIvM/nYSxcETj8Kx3eLgIv+diQ2Dv9Ry8Y9cyPyECyMPA5xjGRJ5n8kDzeVhr\nJTQw5ubjGNmN3AR3g5JqHvIsFNC1bSvFYNyAeKD78vDRI1xd/ITaxo022mj//7Q74SkE0/AOsJJL\npxVdmG6DeA2CPQKJyjEBU9AwXPYmaxxXnYUtwoacdXcYBgYYWV5NSaqL5bs8uYd5Fnn6Fc36z589\nw9VVcNuePv0MAPDs+QvUk+AGMiuNK9kGZ6WcmuXm+r6XVYRTdRcXgYG2Wi+lJuSQ8uf90QzLZdj/\nNTWZadYrwPPqS2EP3bNuu8K1D9ewXROYtp2hd2FVnepQvmsyI01XRM6O7iKcR5K/BQDYoYOllblr\nw0raboP34/otDFVHFhymQImAjiu5xkOhpHCqLNkzI1CxrEWKju8PsgJexZUWCN7SvpwZp+mK3EAR\neMvcBGMQS7fZU8gNSgolahoTNamtHMwmeHg/3KMFpYW3XYeejsHekjS4yTIJG6yErC62mUuaB7GH\nwK9bqVXwN2okrB2CyAyAlsK1sihxcRnGgIRCrOVpezx6+AhfxkZPYbTRRtuxr+UpKKWOAfwygD+D\nsAT/RwB+H8DfAfAtAB8B+Dnv/efKySoERmNYmfZiVlqnrLOie8CrvEs6Mwk2p5S08nJUT5+xpwCV\nNACKMm+eVleVpDdZms3Ra9+GmM4PvWAUhs5ts9kk4GB47/zefXiSa+MquecvQ2x3//59WeH4krq2\nx3ZDNRLVJtkT8Najxyi/RSBbR6y6tpU4/YqEYRdXl+joPB23OOOGrXCiDZExU9EN8OSVDIRLqDwX\n70zL6Ih1CVEEhZmHvcSvjjAZxToTcKhotVRTSv9lGlkW8QsAyEyJiuo+uPVblKKrkBNoJmIzWSnC\nOE7qP5x4cJa8OgZAs0wnaT8muWXCfPRCQDIoqFFwRat7TbjAoXU4I9LSOXkHz1+9wmpDKWBKb3Z0\nPm037ICxcidv0bLYZ6byZ6kXweNlu92KHBuL9kymUxQIXhR7rA8fBK/G9h1evnqzmge2rxs+/BKA\n/917/xeVUgWACYC/BuA3vPd/XSn1iwB+EaG/5I+0QHMuqPfgbs47moejQhcepE5FheJYNeOkrFYY\nZUIfVYgCIpEBGaXdU+Yjub1lGDhVEQZtpmLefkXqPMMQ8888+CaTGT56GsRV+OHNRIhlkImrJNUf\nhej63aMWcazym+c5OsoweBseeguPmjgI+XkAEI8PanRNGCgdy4b3/L1ewiMWSjEZRDBkILc2gwJy\nFkRh8JZvj5f/CIo+DPK7KApPDLvlRsGQqjArLBuTI8sIbGNlqbISQJcbuPD3tMmlnR4z/SxitkT6\nTBoTH3j+gZiV6oa0OoovBvuW5UYmqpImlBlnupRGQyHFfbrfDy/PsKAx0LJKNO0201pCIqa3D9ZK\nqBrVtvUNejuXdyulpAM1Tw5HR0cSJvE+VutVDBt4EqHJ7PTkVDJAb2pfOXxQSh0B+NdBDWS99533\n/hrAzwL4FdrsVwD8ha96jNFGG+0nb1/HU3gfoRbzf1ZK/VkAvwXgrwB44L1/Rts8B/AGdColZAJm\nf+2XODvrYMnVFV5/XklKkoGbLMuguIbA82c8O6t9cWbAJ12qOQcPJxz8riX3m1aauihFq2+zCa58\nUeQCIH1KqcNPXmxwRSmsGbX+4gW3bTspiGFXMM9z1NxYlFZL5v9rpaKiMusyDp2EOywrlpUlCvq7\n52YmQyHbCxg2MKNRxTCAlY+LMrIWdxfccAVJcx4g1C1wGpFLhEGgXj9YOG7hljF4aZBTs9ecQgVT\nVNAUq1jy4PKKy+QVlIrye0DQrvTkGTKHwVkby5Lp+Kx8rTMjHmJaUs5hlACNiC0ES2rgwqGUBdC0\nAdg9I77J/bMTPPksSKmtCBzkY3oo6Q/BPsltwip934s3wEAqe4hVVYkHELt9e/QDcRDIiy3yAv3A\nWqYUOvEzYnKcnZ3dOO7n2dcBGg2AfwnA3/Te/zSANUKoIOZ9Igy4Z0qp7ymlfqCU+sEV9VUcbbTR\n/vjt63gKTwA88d7/Q/r/30OYFF4opR55758ppR4BuJU54b3/PoDvA8Cf+hPf9tb6PfCFZ1wGYZSQ\nQUTX3yTNZBOvQFP7MGkvp9PvCdJI+01Sko4Bx0HKojOwTBh9DQM6SsG1Lb+2wmR8+SKkhlZrYKB0\nVt9xN6CwEjRdj5qYeAwEHh4e7ao2IypJN5utnJumC51OD4RstZxfybXIKi8t1siL0BkybsdOTMuh\n78RzYtBUJwIm0k4vNl6Q2gEk24gYDFf50TYWWkqcWTgmL0rpXqToPDyU1Cb0zKyke7dtWlQV4Tns\nibhYgsweS9u2sVQ6AZ35//ul02lLdxl3Vsm1aq7M9OGYtSsxm4Xf4LglMtrRAWZE+iJOGTqpQNVC\nipOy8FvEdlNGIxt7DtZaOTcW853NZjjIGWsiluPBAY4PmS1LTM+aMRojf7+pfWVPwXv/HMCnSqk/\nSW/9DIDfA/DrAH6e3vt5AL/2VY8x2mij/eTt62Yf/lMAf5syDx8A+A8RJpq/q5T6BQAfA/i5L9yL\nD/G/Qsp936PaegftqRmrrD6VINKyKg82oTCzyAVon3FWFmkvp2Jlm3Q/ikItBcvD0fKnXLp/prZG\nnQZrw/br9QYDrUQNzfxMjDGmkPixpZXl+PhYPIWGq+S4j+bQRdINrbgX16+kbkJk362PTXi5vwWt\nyjbZH5+31rksY7F5a4Ys2+3FmPLJpNcFaz5kRjIAIm7D4iVKwfM+ODZ3Co5+K05l9q7FpiFiFdUV\ncD50vljh+DjExAXdv1wrHJHeAQvgBmn/cN49CeOIqM2kTuo54quItrBITKbF4zTicVFGABpTFjIh\nrODk6BBHFP8XJqSFt12iH6F4LEc8Yz/9mNq+9+N99ISjEIuWcdKzfITKEsyGq4tpp84JzvSm9rUm\nBe/9bwP4l2/56Ge+7L7CIFbQBCqxq8hawdpk0Jy+4wGcZdE9zpjf0IpLHPUJOV9tomvJD4ZKOkbv\npNt2U0fSkNYOoibEzT/X67WAXHw+zsX2YhE85ZSnFwCT88/3zu9jwuW6VA/BP7DJjACCl1ekBTlf\nyXZbCl3gLQwxB7nXTUXlvmVu5MF3kmpUSY8BfrANNE8KGSsf++SVtnNcMNaJbHJMHVLPDudAYsfI\naZ9ZXmFNfIznL8ODdHm9hDbhWCuaEFuKJxbLDaYz8s1pwTiochwQ7+GQ9Arfe/dd1DUJpNBkMKX/\nD8MgWWkp7lJJ6hK8+yxJVXOKm26tD/cQACoSMDmYTXBERWY1qXEtuav1YDHwmOF6iwRe44fdOSeT\nACtDK9XR17Qol/M4sdZKyrKmoq3rfi41N3Niytb0rBxOp9geTvFlbGQ0jjbaaDt2J2ofuKJMqVgJ\naaTbD716oDC7FWZD0leAV7w89zf45bGhqY06jCyA4ZJZm4kqQw9LCrguoxmagcZ+ELc9MugGCQek\n7biGpIlYRozLtrthkGo6BpDeeuut2GKcBUaS/gkswXXxLKTAlvMFLqj6bU61D7O6lq5V3AT3kEqn\n750e4/iQWqZpDrk6OFqdTMkEmizxFLgVGpdLO2jyFKwhF2AwopOo5ZV+M5dDOfaWKBxYrvGESF3P\nqNVZOzisqPLwOYnUXNKK1/QDHIHOHTVNPZkV+NY7IdPNRK/FfI733gvahTWBf7yyA/5G/46b0GMI\nKTns2u/kZIySWgZWEJ/UhZSmT2j1zmlsdsoL+s31M0rfZDSm4Pp+k+S0WPW2fhxXVCYND0lnN1Xw\nwqg4FquyxOWLm9f6eTZ6CqONNtqO3Q1PwTl0XUcz5B5GICCXo9K2hO469LAUj7H3UGotHoJSu6Kh\ngL7ZxYj3nWznnIsklr0mpBpeCCo5AVvTyQpVyQ0FGFwysvIzGMa4QJ7nQkx69DBw1I+Pj7EhLjvH\nkeyJXF5e4oMPPvj/2nvTWFuy6zzs27uGM93p3fvm97r79cSh1ZxalNQUFUWyrGiIRCKIZEgWLEqW\nIQQRIMcIkIjQDyVADMSwEUcBHCWEJVEyBA1RaIlSYg2madjRQKlbpMgmm82e3vzefdMdz1Cnau+d\nH2vY+5x7+/Xt6fW1UYtgn/vq1Knatatq77XX+tb3AQAuvXQBAHDxwkWtqbhzS7QmNrSdC0JCwmvd\nUyeO4pGHHgQAnD5NVXO9TkdjJUYrSW2MLygxicRCDJTM1cYYhKyZ/RytGK3bxSuha7l2dR1ffvY5\nAMBwRN7M6bMP4IPf+AEAwFeefZ6++8pXqa96FnFpzoHY3RtYX18HQFwWALC7s6Mp4rP3nQYQSVZg\nQmT91vih2RNTQEgIejSdGdOWAs7qiIZEWSrt3bzyU1FAp9zgY3wqBhHjaWKAUYLVMbUa1Nug52U6\nncJ78i5L5vjw3mtwV2tpBIzmPSoNMB/MDsegEAKm04pulLhrxeyLB2uV5UbIdE2W6wuqLpf3WmMg\ndQWziEVx5XjwCTYGcdjN895qJLuRbMKIGZnrKca79DBPhsw7OB6r6GzOT10BgxKch+cbVEpJMryK\nn548RaQYq2tH0GfGnY1bt/lc1J7nnnsRf/X0FwAA13n58N73vAdnztDDf+H8eQDAl774RWxtEmZh\nfZMw+Tc26PP8tXVlDvqGnAaKxx97N8Y8EFlextiygyCBMTsbtMxspkHcIG9ZliPocoOXTgWTxYQG\nWaDvbnNRzksXLiha9NQZGpze9/73YmWVlgHXbkq5OA14WVFooLZgjs5uUeAdDz0EIA7k169dR87C\nQVJrlrF40IkTx9HpyZIiMj57zL0sPiI2kQnIJTL0GN4m9RlFUSqPZpf7T8rjJ00Fw8fqmBhodPNL\nVed1YpOgeiLEpxJ1gpvIsgyeg6Fjvp8BAZ4Dsw0HV3NhezJRcAgQoPHdrV0+tNZaazN2aDyFuibh\ni+gpzJKneJvFpUIjI3asjFOqNhOrEQ1jC6SUdjqdKlWXqpL5XAN7U3bRKJXJuXQegUU/Ic8yFFwx\n6dll6XW7mgIS+rPMBRieYgPP+B125V0zxfJxqrQ7ziWuWWG1vZZd0Y2bFEB86aWLmEzoGO98x6MA\ngMceexeWVwjFdu0aEbvsjoaaBhO0YM3XW9oCZY88kZvMPL25O8bSEmkCWCEwKUoY6T8N8iZutoAi\nJFCb5fDiKUhlYxBtBa+o0iF7JJOqwpEVuvaBiplsKVa/w/ddUo7D8VgrEIuc0n8njx/Dufse4AZx\nqfrqca1KzDm9GdhTqxqgEOo1/g5ZQAjyjPGhGosgnqSI3xqplQia0tWlQp5rWbfUKBTsRRhba3Az\nS/pPAuLqKVivf+tnIrsjS2Ewd2nmg1LGFcKojYCKK1RlCdexTOgDj2b82jgaW0+htdZam7FD4yko\nrRZEJEFUj4QiLVcZeQnE5XWuHkUE4YRYG8HH94j16rJkVD6AeqzrMfFEYIKeQ4JAhXgACEqvlusM\ns4YdYdHtcnts4o0kqUWA0GkrKyRkevLkSQDAYDDQYJwEqwQDf/bMWbzjEfIQdm4Sq/N46ya6XOOx\n1LX6ucspxg4HGleW6fhHlpdx/Bit2yWN1i2MaldIWtFkuaYWVcxWdR18LALRoGIeFY3YO8k0sOtV\ngG5lga734QceRL9Pa1wBqFkY3L5OgcMF9lj+0yc/xEeIs+8qpx8LY1BmMaALAA/2eshyQWKyt7G8\nxP1pNWBsVckpCvBJUNn7JuqOzK3vQ8J7IKjOLLMaaFT5Pz5oZg1QCP9DQqijzxjvhywS/0ivaXA9\nqHCf42enntZo9HnidiPASpBcOpxvU1HkGhM6qLWeQmuttTZjh8JTAGQADUpyKTBgWcLmRVA6sTR1\n0zTCvJPWTEiaUmYzThPlXRVP1eo6N8GUgT4iHFoUuRKlllqbz2t17yLPgPASGKN49MVFXlvmwFhZ\nj2h/O5L1vlFPQdb0nU5HeRSaktpxgr2I06fOajpz6yK15+bNddy+/DKdM6fjfvS7vj2pWGTGKI6K\nO+d0ZpS6gePHVrHD+ogZr7WzvBOzCFoayiAwH6AFkwLyMRbBiJch1Y/SVw0Kvi9nTt5H1ztY0eyD\nUtLBYKFD8YKcZ9z7T5zm40PX0JoyNlazH+JFLCwsKZWbUOoLs5MPUY9S04ShiWK9HC/KbIaQiSsp\n1xBzh97OeqVFlqHLla9deU6yGONSj7UTKdUkEeb1mhLws3KKRFi5cEgoUxNidWesV/F6X7SP5PjW\nRPLjA9ohGRTihcobNy+aYYxV6jAlGnFG8QyFdFpm1U03GhiKeXcZICJHoonusegK2IhnkJJl0aMo\n80z5G8W9n04rLYs+eoy0HRYWb2B0h2oShNijYm2A42snsMhposGAPsuyo7lrKRXGktz8DFvM1rvK\nOPaOWUPFL7cgCJtEXm7CTL9DLqvudLtYO3acjy/6EsDSMg1OTcHlyUUHhtOxTkV32IU2Nj5g8mkz\nxSkIetGK6C8KZE4GVcGOZFrcI6K948kY6k/LQCsiwk1MG4rQTZNZ5BxU7DMWoSi6KHlQKPg7Ycq2\neamYhCCIVuRxacCDgvVZVIoWkh9EMzp4SE2K1UFP0s0SKC0zS3SBAEIZBwXDQVNM6RhN00S25zm8\njHNBlxLpkiXT+hppXXzpZSk8FYxOotVxUGuXD6211tqMHQ5PwTDCzBid5cVilaKb4c8HaLISV66u\nZxFlQFx6mK4APyJJq4z6RVHA9GWWlKVFM3MOACgS8IgsT7oMFHFVDBwKWnD1yFVsspCqlAE7/t3Z\n02dQFkJkKkCrHpp6VtfCJRNByeeqN+SijC6ZBMm3s7OjXo8AbFZXadZcPrKGsifXyanDsovAnlOH\n05V50dFtSvSqPRoRp5KuDLAwDFCKqUsp285gGu5AptIbFCW6AzqXoPRGwyEqYcueIyEJJkRkKNcX\nhO4AWY8py+SasgIFp0Qje1wEtqncnSpQZQn5SSTdDYoujGlBapdXj08eLGsCpCq5y56CVGZWVYOa\nffopP5J5YTVI3ZQMhnMNQiM1N+IhxDSoeANCnmOsTZYNMYVtdXnE1ynIytzCvrbVQ+sptNZaa7N2\nODwFAIBQVUmQRbfyv+N3ss53ziVVkjHtOE9WkfLoi0nA0VrAcjwg5+q3uq40biABqlIr7rx6JyoE\nOq2QG5qxjnLa7MSJVVxmfL7AUoVkdGVlSY8xYm6BEAI6HBRUslgb52gJitkOq04FiynzKUhwbGXt\nREJ4MssR0V1YRsZAG/UU8o6CkDKGJpusUE0MP3cPqF5APDGeLUOuTPAaDAuRU0KeMFMIJViAFzp5\n7tuFTolFQ0CsRqHmUV9R+k28yFAOVFNDLM8j2YtQxgmwKAQfocPap1FMVmDwGeIzJt6ACdE7kHhD\n1Dt1GoBWrgWOH/R7JRqhFGTSFxdiIDK3ck2RwyFqT8rz7ZN0MJ/Sx/S9gLryIk9gzTG2BgChyBPF\ntIPZ4RgUQlDUoZaSinaDlD9nmeIU5IEMwWlZai3wBhtdtDBKBxQaJMQVLVR5uQMhVGlEv8AEBN5W\nMwOyyMYFVwPcpiVG5GXWaA3G0aPEEvTe9z6G61zDcPU64fmXV6R02cww9gLExiQPhTpw3BfVdKov\nebFEg47pjIEuHa/n6vRX9LfqIXBGoOjAc5ZAGJaRdxQrkCVqz2meHEBkBs6UARKBa0KC9bByZl0m\n8ctmvCbuYzmwRS4DhBQHea/3P2cCE8F2ZNbGa+EH3aFA4GCo1KhQ+2OZPV8A/dskqMWZa+NzQJrj\nNMujRXS6nPFayyABb1dPUcjySxC4PHCUmUVXNCz4NZtUU8XdRF7QiJAspd7Z01Jqt6p0SWGSLrLs\nqwAAIABJREFUJYMWXRnJwBRJVkWWNpIFsXDCdHNAa5cPrbXW2owdDk8BASL2qa7qHAmGcwHeiGsZ\n3b6QhMEAmhXE9Zw0UVoeoHqKsiMBvjSvLOXAPEslx/BK6RYDj3JOWWI0rlEswjFGDZ7anOBd73yE\nv6f91o4TMci5c/cn5bFWr68RV1Fk71JiDe4Olw/4mnJkhlF6wvRsfPT5ZUrkuoSQlYAR2TW+7Xkn\nphPFXYZJ8nBCriL3JMRUne4fkYE2TVPKNXHHaKCMA8oAYCRKlxlolfb8/bexUtBrnUuWBBFj4FVm\nuPm4WkDQWT6SFwb1VCSFGnwiizf3iZnvIvu3eAYSiB70RBIvU5RoR5ZwWRaRuzx7V1WtyEStNeHA\ntw1G6enqJqZ0tX7Cxt/NLrOReBjJfTmgtZ5Ca621NmOHw1MwTCiRBHpMEtwCaHaW2gQdDb1LuBI0\nwQQoalH24xm9qXV2l5hC40KcJYU/IMsiMo23yextQkhmd6nLsAhzTMkrywt44on3AABOniYPYWmF\nvIjKBa3kV40FE0FXwczNHFmmk3fdSNVoUM4tqUrM0KjgqhC06Dwegi6s9ZxZprUDeUjWnXN6i5K+\nDcEpPFNmyACv9ypqTnDfIajYrM7sMNrPep3GaN3CHpi+iceT6sHc2sj2rRO/j7UmJrlm0MyuIQUN\nIDaAANPmvJ/Z04c91xmZgRtltc65jwZdEdIttGK19BLojvfDd+i7cVGj5lm9Yt4Nl8QF5tW66ukU\nLjAdoMZVTFRUU7Af35OphcHe67qbHYpBIYTAQb7IuSiW3uh4rwWv4PTFFPM+U5p14UYUqHIIToVc\nFL5sMl0OyA0I/L/0XOJ2FmUBcO5d5NfKslAB2gmXCJ84sYaci2UeOHcOAHCFOQl3xlMMx9Tu8Xik\nbXd+9trlpSHCI0EXCrw4U5SeuvfBIjOylEheZNCzq4xKvIzITOTyE05MH1yMYAuqU5cPHgp51sEh\nWbJoTE7ccodcBm1x8z0dCYiDHoyN5dp6w4UrEbp2CokbbDAbPAsw0dWHNpw+nVPUoiIW0SSMSPKZ\nPn/zn/FF08EheD2G7NdleLzNKLBI/cDNyDJYHvyEqt/5gCk3e8j7b21TVmkyrjAV9XAnE5xXwZzg\nI8OVLosE16DVgD5BPh7M2uVDa621NmOHwlMgo6Bh2ONAigtr4wpBg0uNpg7T4JgTuXkJrAlRi3eo\na6lXYIlvW0KCiF4HW58U65DlWUyHRnk5sqZpUHJ60jDWYDyaYP06kZ888cEnAQDX1ik1efvWLdze\nJLTjux5/H7faRP6+ufJun4z2ihMIRglMBElo4XUppB6CznxOPaHccJmvdcjZe1A8fePickprGCQ9\n55JZVSp7fHQCBIknefymQcZp3qBeWBTf0ehiBnVxxXvQJQZM9JikGXCJqy/uo0n6jTepBxOXD+ph\nBIeghV7cV4ioRVmGGR+vcz7Q6JoaXtLB3M/CjRmyAiOu7Wi2yRus6wArmBXB0uQZSgjuhYrj+ozS\nHI3GuLWxOdO3xhpdFrlkmSdOVGZif0i7JUWP5mCpydZTaK211mbsDXkKxph/AODvgebpL4Fk404B\n+A0AayB5+r8TAkdGXtECl7JGttt4Dt4jeA0u+STtZmXBpmkdowg4MQkuynGAuJYPfor58JbNYqAu\nSzDkANBMJ7pNSpA3b91UUIqQeZjRRD2Kl15+EQBUj2I8GUd0pWQOs0wx7Q5zsyCM7mg0eOY1uBnF\nZ+sY2JNYi8xuyYyLnEFaLov4+YyJW02YTUEmjSRkoN+zzesalz0yPneoG1gnfR/BNELeohj+YPS3\nIvAaU5I2pux04q/hNaYQA5iaypX2yOyKEOMBGlNw0UPgbc479RA0YJd8zlfuNs7p3wJeGwyYwLU3\nQJerNcccgxqPJ5GZOtag77nbHQY9DRYWlMSlaSg16dKAsQQOfILYnHuWvY9e8UHtdXsKxpgzAH4a\nwAdDCI+DQv4/BOAfAfinIYRHAGwA+InXe47WWmvt3tsbjSnkAHqGBBb6IA7pvwHgb/P3vwLgfwDw\nC3c/TEBATanJTF0DALGCLXiL4AVcxJ/BRmy6EKvAK816XnC6KOMoMDzQiAgpHb+uAjKW9BYefZvI\niBeCIZcR2BqtZehx7UNmAnIevYes67hx4yb6DF559pkvAgBu3iHa8p3Go8P0ZNdvU9xha/e2jvYy\n0wqMFXWlgYYaoqfZwEhQAaJvkaTZeCYV/oOqbqDy8R2ZCzLknJp1fIxpM8JkSu1EqLgPJMVoYYLo\nODBAqfE6+1U8mzUicmsaZMqBEaFFcs9yBfSHmOq0glcXjoYM83xlNkyRS9xCExLR27BzqetUqFU9\nHO/VG9QamdAg579FwUsIXgyMZn6kpsHD6LNmua8WmNOhXzZYYujzdkmVs+vXb2A8plgSF1OiKDKt\nFq2YlKfkzESJGjl7wl48xbwDL5wM0KBSUs0p8YbYPWbOe3g1e92DQgjhijHmnwC4CGAM4I9Ay4XN\nIBEc4DKAMwc4GqUFk7bPipoCBla5ALOMy4hDUAk3vaAs03RMNs/0jBBz14rr9/G5Qq6/M5oSY1cx\nwcKLgvFtFmFZ6vW0kKea0IuRG6MSYutXrgAArvOgcPyBB1GLe6/vilc1ZnH9JUhoQwz+NToQxLy5\nSXLv0vVCTqKiLSai3KSeo+j1FK8x3qQ6jarahYEEYfm48pJ7o1oKWlocTMRyCPaDB4d6WsFzYDcG\nEC0EdeGED9HEPg0husT0pQNcuoyS1KgMPPHuheQeUX/IdrfntQgh6PIi6KDgoA+hPif8z+RvQReO\nhyNdmvYYn7DAAeeyyLXva57Ejh5fRTOl73eZ0/PWjauqRr68QgQ9nvdf6Hcx4MDl5pCVtJtG+2Ee\nzUvb9g4AgoBUGqdXsTeyfDgC4KMAHgRwGsAAwHe/ht//pDHmKWPMU9s7o1f/QWuttXZP7I0sH/4m\ngJdDCDcBwBjzKQAfBrBijMnZWzgL4Mp+Pw4hfALAJwDgoXOnQghUOh2FPWer64q8QE8INRjzP808\nIuKdq9MKi7ygkTzLBMiRprRmZwBrZoN3ck5BNEagH+1DPJH0neDcvatVX2E6oQGu1ylx/gIxL08E\noCQlr+MJxkKswYAVOI/QSAqQN/GMlCfpSjA4yXgXg2Za0tsA7K4LsEpQoLUHBoukt7C0IMzGgKlZ\nE4A/6/E2ELicW1x5PkaZlQisL5Czx2CNVYkycDy50FxplFn34rlk2R6wU44QM4saVJRS4EiLrTWk\nwcdZch9PYd7LnE8vy3dpyjf2Y5jZpmCmJE0ttQRFkev3Uu3a6UjVpkWnQ4HGTS6Pr3Y3UU+5bJz7\naGnQ05J58TY22Cvo9foxmChLyxAUhepDDKDrdc3sTf9u3F7v4W72RlKSFwE8aYzpG3pzvwPAVwB8\nFsAP8D4fA/C7b+AcrbXW2j22NxJT+Jwx5rcB/BVIoPHzoJn//wHwG8aY/4m3/eKrHYvg7VJrYPf9\n7HY7KrcdmCC0yHP0ugvSIv6sYWzFx50D2oQIoklTnVrlJ8dIYLESFxDwkvUOJatBLXP6abi1jUuX\nL9FxdXayaDgF+Z7Hvg4AcHOTgpDn12/j+i0iVH3hORJU3d7YQp9TWJLeUs8F0PRjjJe5mH6U4KKv\nEYQcRmIhvKb3dYNyiYhMenzXh+MdjJhE1QkdWjWENcwhYei3jo/ROMCC+R8y4YHooiuzI0N8x0wa\nO54CdZYEOgHyOkK8LgAI1ogAUkwtS4DZWt1Tq1tCUE8hjSnMz+4pj8b8thROHz0Fj+hJhmQbfc6T\n9uR54il0hJ+DgWFZpmS+t67RPfZ1rS+c6JwuHFtDj8lnt1mb1PFyuigL7A4pMFkreKmIjNr7wJfn\nPQUT0hT+wewNZR9CCD8H4OfmNr8E4Btf25EiccQrDQp5XsQXQpmUOujyoCAv/rQaInBBVPD0cNZS\n4hwi96LkqA1qBCdBMyn59fpwWuZ37IiQaFZq8UuH8fojNwWTNmGR2ZkvvHxFpeakpuEmMzHdvnEH\n4xG16fJFWmLcuL6Oh7hGQpBnEpALTaP5ciT5duOFBp/dSFdHMpgmbgOoxsE09LCNNqkGYzieqFs/\nHdHD5/0ETU2DV2bibwGgnjr4huncOQuRF10Vb5UsjsjlFcZjKlF8Qf5Zq0FzJwHNLGYkBEshAUcT\nbGRdpp9RAFaXmVBTfIK8+EnQcO+AEX8bXe2YpdAgrhZEJf/ltmZZhi4PAkvMzi3LCOeccmeOtymI\ne9999ym5jmQ1bFFC4ssSSF9bo8K5O5fWMWTJt6Azl9VAZwjx4uczDDo4hBhwxwEHhxbR2Fprrc3Y\noah9MMYgz8oZfsVoES0n45wQpHS6XRUAiWklr5kXqSxLxUdcI6M9j9TBKSqyYS5+W+aQKj3H6bsp\nB3XKbgcVu9OjDZoB7qxf16XE7g6lmm7fWscip6fWN2jm7UswqijhpjRrb7HY687mFkomRJnyd8rO\n5eq4xEl0CSIun5dJ3qmn4FjgJmhq0qLhIOg2a0FcunRFU7on1igd1utl8NUun41nd14CbN/exKUL\n7O3cJon7peWjePjhBwAAq0eXeX/uTxiMR0KXR8fq93uaMg7CtVkDRoVZxUPgmTpBuOqM7n0sd06+\njSXQs060MdETUQ8DIXqDUoeQcjlqCUH0I+aRnt1OiS4vAwShKuzcCB4VL/k+8J53AQBWlleUWq6a\nChGQxxYHFnP2Rvtc3Xvh4tOYcAmlZVq9yjXYb77XZfG8x2AixuGg1noKrbXW2owdGk9BYgqvFBAy\npkGnpL9lVC6LUkd24Qooy56SVdQsFS/rN+eceg+yxrTWIYLtKv7DwbCMueOZQjhVfWFRch3EjUs0\na2bBaSXhtSsUcFxeWlQG4ys3yKPocsrJNU7BTqNdmr2HO0N0eYYYb28BiPUWvmkUgKU8Cd5FD0HA\nTq5JgpNzwTnnNDW6tUneyaULL+H69esAgJOrhLB8xzseQslI0GZKcQbHabTrV9dR8cz/3Fe+AgBY\nv7GN972PxG+f/PA3AQCOnyTy2mkTMB5TewQkZRPdAieUd84BGQcihTtBdRfMTNwA4HSixgvogyj0\nxFOI2+gPqwHDNMCosQr9nUfCgCpno31Tno+EvEWCwrJslzRyWZYKGlo5usL7A2NOQVeI7en26TkJ\nDe1/7QrFfC5euaZELXIpWVbE+53GFBJkp56MLe53MI/hUAwKgSOkKU5hbwS5BkAPaWapE/OsgVPi\nkBiYFI+zUQRfXD54feh4aeEbfQ48hFGp1k7NWR9RMHHVeEIsxQB2dujlPba8jN1tQivWHFxaPrKC\nxgjZCyP9tsb6b3lJdhkWvb5+A0OONMd+SWCskmlQpZhGZdEENeid2xNpFlGQ3BhFSMry68SJkyqj\n17Uy4BZ6LikaE6j3iRPHYMHIvQXCPNy+tanZGIF4r1/nIGfeQdGjJUXBkXibxSIsYZA2iIHf/R5b\nMzcqJO9CElSMgcU0mEjHj2IwM8fRY/CnhwZyI0NTPL4MyBK4M8boQC8BRpHEyzOLkmnohQa+bmp9\nFvqMNZjuVsr23OOl8IXLX6BjVY1ibCpRX8/zqI6eLI/iqkfaGwc3xekcEK/QLh9aa621GTsknkJQ\nVNx86XSaa64qmRFphmnqDN0OpYLyTNxTD1EnsUZqJTiPniW8/oq/j0jCLEj602rBjQSEhFPLZxbb\nu+Qh7OxQsG2l38dlxin0+1wbAKeeipBcpEHUMRNwCM798uXLuMW1FMeP0izcjHe4ExyU7bqO/aSy\nboLMC0G9HRWFkdnYQK+97NPMcexEjtWjJDq7MpCA7QQbtyU3LksWakan7MEEmvHPnCUP4PTZB1W1\nu+IA6ZTTod1ugcEiEYd0uQS4KEv1wrRM2YdZyrc583NLIlo8zJXY638Srki2EMLclv0tBBsLJubM\nIOJZIso2g2ePT0xS10VR6KwtBWVZXqDmZk+ZpbM7KFDmgnykfry6TsuHynllvDa8nGqaWu/p7Lsy\nm7ZNk6j7eUl3s9ZTaK211mbs0HgKdV3PBIH2ErgakSjAlANfozCECSwf1mEylMIo0UnOGglFKSQX\nBUa7NAuOuEzVea/Vg3LGrMhUayBouSzNasPJGBu378y08c6dO6i4TatHiLk57w3w4nkq+8iZJLbk\ndfVkOt0jgnrx0qWExZnXgDLzJUE0qaAMLpKGimS8h9X1tuVYiKAMjbUwErRicdNisKL0XVlD/bE7\nbGCYhLbTF+Up9qTKHjoF9WlTU7vrOug5lhnAk3NpdtHpIRdPLk9UnpTMlQOpLrJQK2gJMfi3R3TW\n18C8p5Cms+djEK8gxT4fq8A+/sR+Zccab0gD5FLuXsTaB71nHMNxjUctM7goW3VzlBx3uX3tZQDA\n1ZsUn7KZRaOCvhLJDHsCr/tdQ/SNAjBXH/Rq1noKrbXW2owdCk8BkFF7b4pFRUtt/F7ASPAV8pzX\nrx2e3cou8oJhyDxj9Thi3u10dL3Z8OLONQGe15F5h+XEFxawyGCkflfo0DlNN9pVAov+gCCru5ub\nOH78JABgeZlG/VAU6C+xmhNnOrd2KO6wM9zF8jKlqW5uUtzgyrXrGueoOG6QcyzC5E6JNXwViVnD\nXAoumFwVnwKDXVBIRWQRU2lS7QejUuq2Yk3E4GByhpH36BgCme51+ijZ+wLLzyPkUYyRhW5hhTAl\ng7EMSkrXvPMpRgStNREPYYYaYT4j5T3mZz1rbeyHucnde7/HKzDG7KnIpfPPpiJnAc6yKf5OMhEC\nAltaijEUIXGRzFhmgVziNNy3vXKACaciz18mz3KXZQLyToHJREhqooCsn/O0qH3z4CUz93lwOzSD\nAvbJR6fmvY8aD8rH2Cg7c6PlsUbzw0Uhywi+Ad0SBQcdhd1mVNeoRRSUi1pWV5awuECDTI8Hlpwf\nwt3NTIN4410uHAKwvEIv+QK70E0GnDxFS4nPf/FrAIDbG7Ts6HQ66C5QLn99gwaFybTCNa6NOLJC\ng42XF6SpFStQID6Q4lLKC2dtARF5FQRcyARvUUD4S1QDAXHwzXLuv6yA4wCtuLgQvIQtVYCkyFna\nzOS6tPIcPMsUk5DB+0RPAhTHi2zO8hZbiAr4fJ4wBJNABpLcoQ4i0G37FQjF3yXpO/mXlueD27aX\n6zA94vxSwlqDKU9Qklo+ws9BnltkfC8qxsusHl3DnS263+MpDw5ZiWe/8lUAwF9/6VkAkVPU2UyF\naI2U8gdEDIIscY2J1zK/1ILZs/x6NWuXD6211tqMHQpPIYTAbMivPKKlkl5ZIm8uLMrClDyddtDn\nmoOClxFFKanGvnLqL3IZ8dg5TLleIOMZcXlpAT32GqT+IBN3takh/vLGBqUmTx9bQ5ePu3KE0ok+\nt9jcpeDd8y+8QMdidaDTp1Yx5RJkmZfG4zGeeeYZAMDj73yYjpFIwQc75/4i9RB4xsgyFY9VT0Hk\n52kr/TgB38isI0uixkc0oUjG5/Iz5MjYwxIQmPNBeR7LQtKxkeAlVj0mNQTzsnjBqoaBLjPUNQ4x\naCYeotlfCm0PklH6KhW1nfuk7phP5yXH3Gdb6m2M2NW/eZPSiLFK0it5ismpzy5dXVd+x9GUnqen\n//Rp/MFn/wwA8CLXlQR+vseTJgZjNTVPAcj0et9saz2F1lprbcYOhacAExBsQ9LySshJX8ksaAzg\nKpHxJq+gWzqAKcNGE9ZdGFgsgD0FQyN1I7OUsegs0nq9WGRgkasx5SBexrPqoLeI0nI8goEiObdj\nWvSxO/4yAGDt9GMAgJP3HUOnz9wKwilQAZdevgEAOLV6FgDw3pP0+fxLl7HNNQTHGSbbBI8vP/UU\nAMB95HsBxJhCUXa1VsJyKjUEjyAQb2YNDkUOz9tcJtNmpC2TGdHGKC5yPsdAIo4hh22kRoPTlTxT\nd8pOVJKS2pEcqjIl61nt72B11vMm1mmoKLDWI9RaWRlkDS3B3yzGQCRFSxqKs6ChdNKcn0GttQmt\nGZlPGJ4l7ZzZRlO6taDJQ9ynlpoN8a7qGtscE7p6lepJrt7i77Lb2OFncszXfuPWHdy4RR7F7Q1m\n9t4dYsRBStGIbGrxwkyMG0jDsgijMiKnkpZlzF76XAXMwexwDAqBxFdCQoIi1rDsmDUmindo8KqC\n9/RATiq68K3tiEpbW6Ng3hJnAbIsiwEqKTBCFpGMPCgYm0UkmbqbtP/OcAeGX7RHHnkIANApAnod\nvpGOXtob69fxwnMUQCo4V7/CBBvGNegxduHcfUR2ffXGTZx/+SUAwG12RY+dJLINqgkBt02jhRqw\nM4n7q2K5wrDshYHJKYmHBGCNAbxgPric2tfTuGQyElmnfYaTcSQf4f7JixyFMi9x+XAhmJESjVDM\nM4dlU9faf1H8drqHCCQyH0FNAsgeSQT+LpYuFfZiEvbZPw3K6YfRf8+LwUwmE/17kZGbQyahOX/5\nCv76WRIBusp1Mc5HCRtZroUADQCn24B0CQXMxj8Pxsr8eq1dPrTWWmszdig8BR88pswRGFNBMkVw\nWs5EYVfNPtYmSUUyL35dJdRfks+loOIC+uia2dnMmEKx5IqIs0CA8B5KNSUdczi8A2EtXl6iZUoz\nHSof3+0b5Ea++NxX4Xh2fPTRdwIACqlEzAw6JWtXrNAMs7WzjW2uknzua+RhnL7v22kfN1UxVsvo\nOO+95r8ja0lQhmfxACJjMtCRWhDux2o0xmSH0XO7lC4dDUfK6xikulSIWhAibZ5GH2NqtGRatj5j\nPGzZhzOSnuTdAzBhijEVvSmtMoVZMztPhRBiNWWy7LlbUFos5f2cJ+9J03QaqPWNegZBlw3RcxHv\nRH7bNDElLojNLuMyOp2O1oxI+TOMSVKoXHsDYKbsMzmnpfztzDlDwiP5VlnrKbTWWmszdig8BSCA\nKgETZJmuryTAAphslgXY+WnkohRlJO+wue30e4BiDwCQF8dRimSaigh0VXHKWEHTOTRMKipqR5VU\nRm5fRbcrykm0T7+Tq/rOS8/TOvL2+jUcXyUv4MQqeSpXrlGsIIeD5WBVjz2WI4sDjVt8hVOT3/t9\n30Pn3pqoNxOSWXOemdoHp+3wHNuQwGCZ5Ur+OhlTf2xvbqHapUBZ4Qh8Mx6OMOX0rmhHTJmd2TW1\nAsi6rHtWdEpNcUqNxxbPmrYcoNunFG1HkKEZ4MbUbxOuqqwyYIHBYjkTzUQ84T6zosGemf9ulnoK\n+7E5i82eK8YSgNmq1Jr7ZzKZKOp0gWtNAse2OkWu91N82eDjdWn8AEngV84copds9C/eP0nNv0oR\nxOu2QzIo8A0JISGM2YOFhWGXVVCJwWfKuRgz8UE7ToJARslOOopk1OyGKWBywQzwINJM4D09sM2Y\nBoPdHSprtrbCkSP0AN+6SbDUhW4fFQt+bN4ilqWjayvKj7i7RYOB8DKW1sOwqMuRBXK1p/UiNrdp\n6bF+jfQlhzu78drZqZNBATPwXyWlRGB3Fuz61xzNH40rTLmN+jmp0PAyYzLa4OvcjoIlc8xEJgRk\n/MKL2zxtpiqB12EGISlIgykRQNcuzV5eWURWCE8hKyn7GiIhOr9U8D7o0iMVf5t/kV6P7RkgUlYm\n+Vt1YrwWbamKefBYYFm3EfN7+rFMQFaDsnFAt3EgVymARgvaokQi9NzBSgPow5r491u1iGiXD621\n1tqMHR5PgTHxQhWW54Kx59LfAshYg6HfY60HZzGthKhDRFu6GAzIbZdg3vISubBl3oMU8gjxSZ73\nAKbNkiqcaeM0tVhNabYWLYSyBAaM9Lt5hfgNtx2wweIuMsoPuiXA2gh1RR5ClzEM3QIo+3TOitOr\ny4Oecj+O2UP4wuf/CgDwDU8+iRFTo2muPhh1T6XdwTcITHgix9i5Q+3avrOJasjydQJdcIE0LgDs\nCh9jXaPLtGCLXPBVCkmMoWIx+ptTwDtb2Bnu8mGpPUMu4hkNh3BSwMVtvXHdKM7EMUP2wtIAhaQx\n5f5zehMAgjym6Uz+Gj2F/Wj+9vCABpOwN+/9fUyXM1K2yGE4fytEMwL36Pd6+gwrz6dzGriW9LDJ\nsqjpoUHN5MRSYyb/TuoGD1YI/dqt9RRaa621GXtVT8EY80sAvg/AjRDC47xtFcBvAjgH4DyAvxVC\n2GBNyZ8H8L0ARgB+LITwV6/aiCzH6pGj6HZ7WgItYrIi0pl3CxQ8u3ZKTnmZIpbYOtELyJSgUtbh\nJWsKhJCjaWa3GWSaOpKYpq+nWpVoeTxWWbCsj9EmpfG2mXV5Z2MHU6bSklmWSnP5uLzmF+bmIjPo\ncHnycJu2ZfBYXSKQ0/UNmt1fep5qJr7hm75J19oi9x5co2BF1Tvwjeo+iMbDzfVrAIDLL1/UFOMC\ne1qFLTRXmHGgb7DUxdnTpwEAR1dX9VwABdskJTlYIEDY1vYmbt6hOIoE3cRzuH7pmmpqiFfw8oWX\ncPkqqWItLNIxHn70QQUtVZNYeQowc/fcWtvgYGAksf2CivvJzdkA9ZyE1k72c86hUSFd9hTyTCsh\nez3ar8exhXLiNYUZzxXTt06/S0FWs8FQH0JkhxNQbnrZb2PtwyexV2L+ZwB8JoTwKIDP8L8B4HsA\nPMr//0kAv/DmNLO11lq7V/aqnkII4d8ZY87Nbf4ogG/jv38FwL8F8N/z9l8NNNT9uTFmxRhzKoRw\n7W7n6Hb7eNe734dOt6eVdkUhijuMcc8yqgJEJGLNbQQeiUJpcCHONrIEFBoya2FFZ7KR8bCO9eus\ntVhmBgsrlEbcukWz3i2mYOtksRJudZViFedfOI8Tx4hkZWmBSTZshfFwm08v1G7Ujn6/r9Rs3Q5r\nOTYTrPDMOWLQ0/WrlwEAN69fx9oJIlitnbQ7i4QkLlKXSYRcgF4ysTRNjfWrVIVXj2s9Ro9jBMcf\nehAAUBQNii61ezKV2Zv1DL3HMpOIXL9NmZKXzr+Ea+sUW1nk73rsdQyHE3S4Pd0BeXmXDyEXAAAg\nAElEQVQryyvo9unaT5+hPrv/3P0KeBJLuRF0trSxduONJh9STyHGFKDxGYWOS2JnGp+TnAMHtizU\nGxQIdofTsp0yQ7/PwK1NPpaLnpZWwCISwOyhIETiDNylvuHNttcbaDyRvOjXAZzgv88AuJTsd5m3\n3XVQKDodnHngUWRZidRJpP9KKW2OYIWdWbQMbLy5XlI9QLcjPIL8sqcMyOxCb4/pwQ9w6PHNc8xT\nONy5o2XXwp6sgqo2R4cHqsGABo6y09UHpsMFTkeWl3U5MrnDg4PWKESevR4PDnUTMOGXWwJ8Vy7T\noHD18iUsH1nWa6CDhCQiJesUH+sJ+JvVo1T/URYdHD16indjV7c7gOH8Oocg4ZsaNUewtoeM0eDy\n4BA8hpxyE9KUTm8BDz38DgBAJgFJDpieOHocixw45Lga7q8fQM4B1wEPHkUn0yWQ1zSecDUmJc6p\nFOxrfDP2aEfsE7QMCFGohr8TceJpVWlqVtCLeWaVBCXP5DtOm+cZVpZpOdi/Tc8VSefJoPPqecWA\nGFievZhkh7fA3nCgkb2C19w8Y8xPGmOeMsY8dfv2xhttRmuttfYm2ev1FNZlWWCMOQXgBm+/AuC+\nZL+zvG2PhRA+AeATAPCB978nFOUSLwViFSCQjPA207JgmwRd1K+IEEilrivU25By34CqFvosGoiG\nww0sLkjQkY6xcec2uoJUYzx/ryTXOLfAFgcatzhI2O0NcJul2B5+J0monTn3ACoGVt1hZSgnACQY\nVfmR9q8sL0JkZO9sE8rQ8mx268YNSCW0pMx88AkuP0qQiVsqqk2Slu31l7F6/Cyfi7yHhYVlTNkt\nkCrGajyBRexL2sZ1Kck5JSWZd0pdColAqmUX2jgHz0uPpuE0pG9QMPN2Xsh9aTQoFz+jR6RclNxX\nr3UqeyU+xj0WooqWBBxrkSCc1vGaZfkQvL5AkkHtNqmnQM/M4oCWoLujYXLfYz3E/BJBiWmSpoaZ\nf+z540211+spfBrAx/jvjwH43WT7jxqyJwFsvVo8obXWWjtcdpCU5K+DgopHjTGXAfwcgP8ZwG8Z\nY34CwAUAf4t3/39B6cgXQMvUHz9II4yxyIsFGJMw8sbz0x/W7oH1knT4/Mjr4AVIItwJmQTdGgTD\nEF8GI412b6NmEVRJ+3XyEqvLFEQUkpXdKgbdRkMODjKieGc0RuA1sEB+t0cT1KLWxLN1xrh+38SZ\nqOKDdGyhM3QhjMwcMP3rL3weH/rwNwMAyq5AvOuoDJXMpMpzwFDjDgdure0gMHCrPyBy0X5/CT1u\n75Sh2JMMWoaquPtSNB8j4Ec0ET08Jgx5djyrjoeiGFUrr4N4BUWRqb6lAJpgEoox4WtIgUU+ekJk\n9jXxhoSUUOVu4CXnFRpfsxZIwwphIQTkqrrFAU8fYd9lIVB6of6zWOIU8/IyxYhu3dmAdxLk5Us3\nQT0DhTtr/tEqAUxks4uxpP00Kd4MO0j24Ydf4avv2GffAOCnXnMrjIXJuqD7z+6jelcCIvBwEDZn\nYQZ2iDUP8iA7LXsW8Rh4CTRWMJZLXUv6XVl6GHZtjZesRlcjPMIEvbRILvdwdxtmaZY3b3FxBS7Q\nYHN7k7IU27tjzQo4zniMmZfPuICulNryy9vt9zDgi17l3435hb108SLWr5HD9cDD99P1mshrqK62\niXJxirHnQF/R6aNhub1Nlqy7M6yRM9hhMKblTz0aaz5e3HbHwbbgnNY8CNmLR0AtD7pwP3IhgzMZ\nAmeRFhi/kXc7yoVpjQzoGRwP5FrHEWKgUYONGk/1cQ15AEsHhXTb3kHB6XJBaPaVTdkYRVuKUpz1\nRgesLKdj5Lk8NzlWuCz++Botoa5fvxH7NqkC12Ug4jbaxyq1exwQAyJA5cBd8JqsRTS21lprM3Y4\nah9CoLLUgChJnhBTAEBAgxCEnzBy/IUw6z3ANajrKPkOxDx3BqDHArBHj9HM37E1djYJkecZA5Dl\nmQaEJlpGTAfpdLvoM7Jyd0iIxoXFJQRP2x5//L0AgPMXrmD9GuECfC30XXSspqpRLovgKsvUN7V6\nSQ3PmrKc8E2Np/7ycwCAc+fO8ndxBo1pNAvDGI4uu7Pyb2RWA4EZV2gOd4ZoRhQ0Hd4mlOF4PIGb\nzs7aQcuxvQZxxVPIigKWvR5BR+ZMNNIddGG6UhIt+wQIKZl4IplNipYTYhRgNiWZys7PZ+VmgH5z\nCMhA0/GMzUgUCgWcj55QLWLGUqtgbZS+Y08nQw7HgWtjRfiFPdDCYoVJeFZXKUW+vFhiMpalAp27\nScV15/AY5pUWCHHVtac/9rPXCuloPYXWWmttxg6Jp9AgTG9TcClIjGA2uGRchcxxpaCSewK1pNR4\nRq+qGh2enQwXPxZcKxHyAs7RuJkxaGdprUTDO45GXBGZZagMV/AxS3POi8BqNMaYZ/wbtygTu75+\nB+94lFKRR1YfAACsrd2HP/i936Nr4Ws4c4pJWi9dxi6Tm/S5Hn88naI7WOTrYio1Q7PV6toCvvrV\nLwIAhhvfSedZWUHNbMtlRtc3roHAXoDPxOsRFtYagfkibEafZWcI25CnkGcUU8jtCA7kkVkhty0F\n/x8UVSpAMp8VAAczS+53SYcWvRzsNECcicyGPWjBxsdgmxxf1s3eJ7LziQcw4wUAMwry856CMSYG\nLsUDDUGDirL/tJ5iUlG1qBCpSNVur9PXmILUKHhjETgXafmeGU7fDkoHM6B2Ly/QeU6f6KIa0/5O\naP4cMJ5KPQQ1rWavtAmAC7NttAn9HZroMsQ0/WxA1Xuod3fQqsrWU2ittdZm7FB4CiRFP0bwbm+a\nTUVRKwTmODAhjqxTXv8OGZI7qaY4yp6BVropMWuWYM557aopirjGNXm+hwZMZoyRc9jZYf3HyYR/\nGCXIxzxTjEY7SrIqs4J4P71+T9eFfa42xMhikcEuHak6lJr7PEfFqbE//4u/AAB89CMfRWBvQyDN\neVliwvwSBXtCNpPrtJCT5twHedGB4b7KmYPCmUz1IoPSqMeUnahBidS9LUtkfIyyM0uplpUdWNWV\nlIxD7NM4q6V6kfyR0tYLiaqVexfFdfVYM+LEsiaf3SM9gXOOYcdQlbHJZKJ/S3vlvhZFoX9HWHTM\nPszv3+12NcW40qN7fHxlFbvL7IkIyKnsw7OGxbhiYN2Q4faTMUbssdQaR7OaEgXHaZzz+lzP95/J\nzP6UdnexQzEoeO8wHm/Tjd6HDAMAMgPk/CBK7UPHWnT71OG9Prne1bSOD6y8VBK0shY2yEtC526m\nDmPG9ssSpMhyNOzLTSY8APEPrl2/jm0ubZaBazKt8PL587SfiOBmRoVW5eEbV3Szd0a7qDgtWLL7\nmeU5brLArBRV7TKX4bCqMBlS2556+mkAwPufeAKrXNq8zYNDllkUUmremUVp0uDHfRnkATbIurwE\nWebUW2eCrDeevZYQUYaaq88FxViqlFzG24wUsdmISRBLROCS3HvM/UsbvaYQTZKm5IfbJ2SH8qv9\nOBfVhfZaNyGDSdM0OqHIZ1VVCQ4jsjIDQKcsdZAX8yGmpWVQKDud+B1PBmu8rPJrR+F3BOFJ7VlY\nWoVl0h6GRmDCacvhZIwtJsvZYtbtnZ0dDPk53RGycmMU22IUL8M8pc6nPb6nj/azdvnQWmutzdih\n8BRoBHMk350JTRpX14nbmYxfcVYwivjKCq5S64TorvPsLunF6WSiRKYye492bmHjjsz8NDOWNlec\nuxd30gsWfqpBwq0tSkkGE2cWWY7sTkcIHF3z7MKLLsLq0TXcuk5ewQYrMxV5B5ssU56zuykEtQvd\nUmnnXrpERaj/6o//NX7gB3+Qzskz9LSZotuVpROZeFWYmbE5teajBLyAZPJODfQ4JcmgJKNl2CFK\nzwn+P8thhUjXzgKnYI3ev5k5SmvaE7SqLFGkzF1k5qyJAUb9WQw1zqQW572FxNts2OsR+b2qqnT5\nJ56CtZEOUDyEYm75M3Mes3fJ0hE26hBT0MtC6LOwAL9G3p3nfl9cXoVl4mBJ6QZNMQdt2+6Ilh3b\nu9vYZFq9CxtD/W6b62WkitUo2S7UW5tXX3slaz2F1lprbcYOhaeQWYvFwYAQnPN8CjGDpDNQHO8i\nn7/MdFkWATaKKefZZ1JV2NmlNdqQR9vR9k3s8raC03dVVaEULKukhHiGWV5e1nSV6CMUea5UZDKy\ne2PQWyLMu3APTDl2MVhZwu1bRBl/nQlKrCmwvEKzyHCXZoBSaNNMjmpM+29s03d/9tTTePd73wcA\nuO+++7htq7FNEhSzca0ps1nGqk0+FAArZjn+DHkDmzNsWXQ8OZllkUKT+Q4YGzUPzew9g41BsRmY\nrogn2rhNNBUkmBgjj17314CZsZounammDHu3AbNUahV7B5PJRL1F2b/X66nHJx6DTT2dfUhQ5Hvl\n+MilijWu5JdYIwN1g5rrIXKOw/T6fViOwViNxfAxDOA8/bZx5D3WbgVTvrxHOFi5s7ODGywtcH2d\nn6ubBLff3B5iKtT0OJgdikGBipsz0JBgdRsQBwlvAoIRDIOg3rKE105qJoK+COo6yzF8zJGLO1kl\n5BmGHz7XNDDycPAxBJvgG6c37cRJYg7a3trC1StUIX70+DEAJBBz+jSRmkyZifnF556l408mWGRc\nvCyXRrtjjOSBlQwCBwGNzRCkfoIf5Os3b+F3fu/3AQA//mNUd9btDzToZ7KYcaFPG/uKX/I8WICl\n5JwEZ70DpA5BkJVBVLkDgi4lkiIsEbrdQyS4Vwg2JC9L9L5N/Ie4/Fo+vBfRmBkT5eXkxQ9+T+bK\nJ0KwglCUQbNpGn0W0oFAnpn9CqhkgNM6ChMj+9lcf+fO64DRH3S5HWN0uaCt2xNEa6ZYkrIrDM+i\n8RGzMsLp2PgCNQ8KA359/doAD50mMeLtXUK83rpDuJON7V1cvU54mv/vmfM4iLXLh9Zaa23GDo+n\nEArKV89S68NL3so4VcuR/G9dj9HUs5x6RVnqyK/SQvpdhsVFcsnHY3LDt5xD4OCjl6CmtVGBiM8l\nLM1VmChHY1fTfsA6pxOXV6gs+fgDZ5WabSpBog1y8S699KLSjnVkxig62NnhVBPXVAiyLYPBzi5T\nevHE2yt7+JM//wIA4PTZfwsA+Mj3/+fos6sqPJYFB77SzL3wXu5UOyi4rFsqIZEGo2SSduISOyWz\nSQVhzVw/y0xtED2zOIu7uPTQNKSPRCqKUIyoRPmt4grQ6JIm4li8YgwEqSh0fDs7O0n9Bi+FbKox\nEj+zbNbLVA/AmD2BOkl5p8eQQHMzHCpaNeR8zm2AISDoDejZKXudeK5iNq1orNXycucFG5HrMno5\nWSW5QMdrVuh+3n+cPNFR5TB654MAWk+htdZae512KDyFEIB6GhB8pN6SpWjQNeMU3kx1f4Cw6srZ\nL8Eaa+AaqWaT0Z7r4HOrbLtCsOmmUw0+afDKOZ1Z4gqYrNfroc+VljUHFR995zsUXLLFWhAn8wdU\n1WmBNRYffPgh+ne3xFef+TIAqAblzs4YeUdSexzwZLcgNFPs8n4VT+g3bm7oLPxbv/U7AIDV1aP4\nz77zO7iP6Bgj/l2nLLSvZAZdWVnBNrc7gQopWWhKewfI7D0HJIKLcQAhBAnivXkluoHGILzGHkwq\nlqqLZ9GYkBhRJKMVRKhzDRonnA9BrykFIQHx/tR1rXEoSZHmea7B2FxrGu5eT7iX0i1EpCZ/NuyJ\nZFmmnsKQdUmzbokOM1l3FmJMQbwLQZ8a5aVQYjxkQWpCDDKJhaRxT0lj8raKkVBV7TCuZoldXs0O\nxaDgncdoOKTnRiPGun6gf6PWQWHWTZUnmKPXroaBlAhzZJgDlNO6QjXifO424QPGo92IZGT3LUVW\n6vH5w3uPDr+8Qne+srKCM2cpwPO1574KAFg9fQqDAT0UFf9Y3NvBYKDMx0KHXpR9uEADSsPXPOas\nSOMyzT8LyHA0mShWQF6uT33qUzjKefAPPvEEHb8rKFCrmZRChGiGWypzJuXS3jtYzjoYIU9hkhob\nmsgcpANB0AEInAEyyYAhZdLitofknskSKqWmF+EZlwQNJTMhrNyNq3VQkD6dTqqkKK6aOWdRFPrS\nCnQ8y7I9wcT9aN/TJYMMeqlATC7PDO8vMPc8z/WcwxFt6/W7WtzV4+BjMEHp4RX7IcHhLIr1QM9t\nkcugIANjQLJUps8e34LGBbhw98Fu3trlQ2uttTZjh8JTCMFhWg25+kVCYrMFHrBe3fsZ2kbB9gs1\nWVOjYQZhcPrM8Sg6GY8wGdPsK6Kv08lY6xBydt+qyUS5GUVcVUbzpm7g+LirPCsPxyMcO0apyEuX\niKzka899DY9/3WMAgF2WoBPvJNQTLHHx03CHgpCdssTmLkvVsVvruIK69gFg1KKfcJrQ5JgKwQfP\nZpevrONXfvlXaBv3x+OPvQsAsLa6gmk1nrkWE7wunZAEAqFIxllEYwhO+zkyLAdFMhor3Jng4zsY\nzo5HkRofF2TJsbyqrsyyOTvXoOb+k7bWrtEUnQQf62q6pxRaAnhFUeiyocgi/kCXpknwUWZ3DZam\n8nKYNR8Am82mQQUxWRRlXFoI92aeK1JSCICcb6KnIPtncdmrOBNBntpcPeCZtproSaTtthk/P6/B\nWk+htdZam7FD4SlQOG86g2Exe4YrO7M//TdSWXlOK7pmipo1DGRKknXnzu42hpwe3LhN7MWj0a6u\np6cTDvBNqihAy8fQtfxohOi+0Gev11OqrnMsv/bFL30Zu4xHX5WSaE6Vbmxt6P43bxKwZDJ12Nik\nGXE0lU9qz7gKmDQSwOLUU1NpCfnKMqlHWQNcYlWpf/GrvwoA+LEf/REA5DEsMT1YU0ulXgPPsvDg\ncwbnIkBJPkMM+kkMQgJ8PvjEU2CMvbBYw0OoPSRWAB8LeaU02zkXA4tuNq1Y11NMeJ0u8nW1c6jV\nk5Dj+73owgSIZOdnULt3PtxvWyr2GnU2Ym2Nm/NstOo1y9SbkvR2ZmM8gB0MUPWJQnCp3VKbggyG\nGbjFU7Aheg+BY1smAeUZ9YQiYK8n6NwDWusptNZaazN2eDwFOwUShSOtltMosFViVTEfwkyUGgBc\naHTEVwIW9g5u3bmFEc8621zhOB6Okc1lB2T0B6JuoKa0skxTRymstq6pnZKGOrZ2FC+wlPy73/kI\nAODUMdKSCNMJBpzW3Fhj2fnzlzBi+fgdrn3Y2GHSjUmDO5sMaGJYsodBr0epzrU1imcUFuj36PvF\nBfruk5/8ZQDkMbz/vV9HfWqEU2AKMGjJTKlfvHMxhiBy6ZISDB7OzM6WzoekvkL0HCRmQdWvtB/3\naRLNF74G7xym09l6krqK+ovyndCtO2M02i4r/jTav+/nPgH4eUjzftkHrdkIQY8RsxXYUz/RZ34P\nHwKm/F3BWZnaQ11Oea6tMcjn4gGzngK/okGew6iUNhYAWQLEUj1P8XoCULv5aMjd7VAMCgEkvhES\n/LqyOsvDV2vcMJFJi/lsl+S1pYMm/GBNGC/gpw3AD1bBD3BtAxrNZ9Pxm3qqOe5KAz2cchpNtMBJ\nBordrW2UjH+Qtj1y7iFdGlSckjKgQWFxcQHOkZv5yKOEXcjLDnY+T7URR3i5cfM2aT1MJ1N4fnm9\nlJYXJUacpnz2a88DEAZnatsjDz/Av6Vr/4VP/CL+zt8mCY8PvJ8Yp4u8wJRxDAMpSGom8JD6BhZ5\n8ZKurFFyCk41LRrAc/q4ZLKQjGF707rRfhORnLS8WQaFuq55WQZM+Z5VXKvgXBNLp8X1z3MNBEuQ\nLrNZVCDH7EueZVnERmj9h9Gcvrj3hhrI1zobaKSXOKZQ6doDggjnSNvkeusKDQd2pebb+UbTjvLc\nWptI2s2W/QAGWu9jtIgMEeXIWAST2STQKAOdrvMiwveA1i4fWmuttRk7iGzcLwH4PgA3QgiP87Z/\nDOD7AUwBvAjgx0MIm/zdxwH8BGjK+ukQwh++2jmCD5hMCMDSzAVuNJXVNHDTZuY751z0EJJqPEGq\nqT4ETwmdPIfj4J3MuKZXYgIG51gB2DQ6i1VcHQldYsQ0XlNJcG6KZZYdX2DAEmqHo8uUsqwqCjhe\nePllAMD995/CmI9RsD7DkSNLeM973g0AyHLyFHb+5R/QeS7fxAKnsLZ85OVrlM2Xtg2nNcoOXdcX\nnqWlywNnTgMAlo6s4Z9/8jcBAD/4X9L1fv37n8Biye4uy+gFFzB1HNhjpmew7F63m2GHlxnStdZn\nsBwMG3LtRq9Dx3SN1+BZxUsA55yClqZNrFQV0M98paMxVtOJMYBoE2AaewyJpxDLnWM1rViUYYvB\nRy39RkAq1pseC4iVoerNuiamOtlt96w2FpqpktQINVrVjPXZbLjytMwKOOFZ5uBgw55a0IUttMzc\nEmsK/VY0KWBRCDCNlx5Iljj5a1DTAg7mKXwSwHfPbftjAI+HEN4L4GsAPk4NMI8B+CEAX8e/+d+N\n+DOttdbafxB2EC3Jf2eMOTe37Y+Sf/45gB/gvz8K4DcCCRe8bIx5AcA3Avizu53DOYfdrW00TbOH\nNktTPmk9QpIO8zqyx/JKGbWVIi0JJNkE+w4A1nY18KaSjNYg49G1qzBhGuE7RYHsCKUAe1wbX1Vj\nBT4JBHo42tWZTWbGhk/w4ksv48jKMreDtRYXFnHyFMUBYGltfu06pU2vXLmFL3yJaiWuD+lYly5f\n0XNOeP1tcwvPhQsFazHcuE2z/cryETjWavjlX/01AMAzz3wZ3/7hDwEAzi4xNDzTsAsaz1wLPJOt\n39pWdawOz3ij7SF67O0EJiMdDXe4b41WX0pAblrXqHnbVAON/hXrDqyNVZhWtSwyTTem1Yx2Duqr\nsYWElyDywQat+DQzoLjZduwHd07/ludI9pNgddM0MR7G1+vqBrk8f3OgODqwxBYkyB4h4YrtAgA7\nyyBtfANw33sV9OXYU5ZH3YwD2psRaPy7AH6T/z4DGiTELvO2u5prGmzeuU0491egeIcPEe2oaK3I\nYmut0G/HQJYEgRrpUR/U7fTy4NgssvS6+HBoKTbjFUQIFt2u4t07/Nnvd7TISO5xvtzVqHm3oHLt\nh05SUPHalUu4c4te+LW1Nf6dV8alhSU65zd/89cDoBLqwSK18VN/9Ke0f+YggS8ZHBoX2YdkcF1Z\nJlf+uRdexv1n6VYIZ+Qffvaz+Mun/hIA8PgD9wMAHnzwHI7woFd0Zge6EIDphLIlS1yzcerEMWww\n5X0uL5QI5A63AA5WajWEd5GOXDM6NnlbySK6D8q8rQRPmYUtZpm6YW3MSMwdy5jkVdfHw+yXkNhT\nDyG236BgjNlTGi4Zr3RQkGfDNQ1QzuIILBJEZT4brISJxWOxtNxr0DG3zKJt43Onv9XmB8WzHNTe\nUKDRGPOzIJanX3sdv/1JY8xTxpinthjq21prrb399ro9BWPMj4ECkN8R4rB6BcB9yW5nedseCyF8\nAsAnAODRB86EajRm914CR0I0IWkomwSSxFOwe3LSgI86C5yKFNHQFNsux2iakBC5JEUVWh3rZ74K\nCIpxmDBRi7XQZYwsXQZLPXW7xTsR9ufjJ89im/n/d7bp88yZMzqDShBy8w7xN+4Md/Doo0Tt9q2j\nDwAAvvDXX8ELL3LXivR7CJhyMDZI8HHEVYRNgwmnsI6foVt0a3MbN7jU+7N/QbJ0//7pL+vyTMp8\nuyJ0M6owYByEpDz/67/3MawcpVTriGs7Gg62oSzRTEUUGGp2DnEYafOSfVKtjjkSl5RvMv3c6yGk\n+wgKdm9Ng6RNgzeR4GTOA5j/jZifQ1YqR6ePS6KpiNU6F4+RBNJT4hf6g8/nQyK4LNcCnflFm8Rm\nuQYftbUirehAjNivwV6Xp2CM+W4A/x2Aj4QQRslXnwbwQ8aYjjHmQQCPAviL13OO1lpr7e2xg6Qk\nfx3AtwE4aoy5DODnQNmGDoA/5pHvz0MI/1UI4cvGmN8C8BXQsuKnguYF73oOdIsOB/hm00oxfpCp\ndkEiHjRzDNloZXTlLYp6RIiEnzITGKPCmxKDqKoK44LGunwOS17khVa4AT1um8FwSF6DyLt14VBw\nIFJmk0tXrgIA1o5UeOjhdwAArrx8AQCwsbGtTMCdHv1uZYWOPxzfQp6R9/Nt/8kHadv2Bi5fpONN\nJvTdoLcIAS81nq6zZu8gK3q4epPiGCfOntTPl16k8yvsJ0QKvN2hgLQkHQYMOQA8fZ68lD/9yy/i\nW54k7oa1k+TNbNwharpuz2C0K3oZke7NzlX52WQGTj0E+XdKlEr9bZXcVKsCbYxL7JnRTUgC0WRp\nzECUnJRlOjnGLHHrbLuNMRpYFO80rdSUY0wmUXFrPnDpGpecA7xfrBBVLzZED1fiDE7eBxSQVzlA\n9FI4xlb0hE3vwHaQ7MMP77P5F++y/z8E8A9fSyOyzGJhQAGx+QdArscbo5FVMZu4jLGAKjLZyDGy\nPNKvex0w+KYbq9gIIedwLiH9ENSY6AIuLaGjUFLoeeQYBQ8Y3UGJy1dIuKXLmYCjx45TO0YNLl+8\nwsegtpKOIbmZvYZz3p7ac+LEKp5/kVCLaysEaf6Wb34CZ05T4PB3Pv0ZAMDF9R14R8frdgnrMJZC\npwBMeMC6yEVTq0eXAWagmjRR8MWIorQTKLHI9BVoArVxZ0QvwWf/5HN48GEKUp4+SefsrRBmox45\nlJ4l0yyj+1xQ5qBIyhRd7fmiJptlUWVZGYmSkuJkkFfGIszaKwnG7FFoDl5d8/2WCvNtNMbE0u05\nuHN6XCnNT4U0I0tzHGzkmZwy/sTDw/uIoQAIdi+yiL6gfrZlF6YQbU8KPlqGwJe9ARZXjuy5lrtZ\ni2hsrbXWZuxQ1D4ABshIw2E+gKRBRWPQKH4duo8GJrM4isfyVXHfaKTe3d1F42Z5HofVECOpkago\np99UU6X+EkHaHqckd3Yiq6/jsE41rdHwKqnLI/RWtYEbN0iYo88Iv3pCJ93dGqa6UrEAAAlwSURB\nVCLn8fjoKqUki8Lg6c8/BQBYOUIpzHd/3aPUhiLDseO03+42iXyUFnj8Mfr+xZfII7mz9XlsjSTg\ntcs9KwhIh4LRjpcukZfy4EPndNapGUVnjU+KlmbnjNA0KgbTYY/owvVr+Dd/8u8BAB/5/m8DAKwx\nozCcRah5VpWCndzAShCMvZO8KHRJoYKtiafgzew2HwwcFwipJFoIugzI5pYlwXu9JHU6bBaDxyoe\ngz2l0PuVWqfeQIpLANJiKaPPnQgQnT59WnEkqUSG0KXJCks8WxcsGt1fFMgH6C4RUrZzjDy0peUV\n9NnTLljMtmD5wGCMelUHtdZTaK211mbscHgKxsDmHSABDSlowwhCq0SH9RYiKjFTLHmh3P0ZSiGf\n4EFWqiUXJuMISmL25+FkBwWjFgVstHHjpnoKCz0aeWshIwleDyxtG03G2OFKSM9itbUbxUpL5v0f\ncvrxyNIRrB3hMmr2MHZ3J5rW7C/QqD9m6rXMGSzy7DDeIe0IV0/QNKQC9MEPUEn0xSs38MxXL9Jx\nuY5DUC3j6VRn0qiqBaytUjt2pZI0BCDMkrMqUQoV7tI9YE9hYXmA9dsUWHzx/Hm6vq+n9tTBwLJe\nRofjFOPdEUqe9bqsUVGNK/UCJD1ohL04ZPDCBK3aBzb+LXLsgAapJW7oGJppTaq0xJcUUlq4GPyb\nD3DfjczVObcnlqAKZE2DyXi24rPxXgFe0g7vPAw/wxl7uI6JhysH5AU9C0eOngAA3HffgzhynEFo\nixSjKsoiXrMELfk6nEdEfx3QWk+htdZam7FD4SkYm6HoD3jmp9FynpM/LzuwopOo67wkWp2AnST9\npPhvXmf1Flysg+DF5QKWVW9BQElbt+9EMVM+llRGZj2rHkLNmYxqWingaKI8ABX6rCDV79JoLzVv\nuTXIuV5AFJ1GOVDy3+s3yRvYZu/j8fc8jt1NpqQf0rbVpRVcvkYxi5zrMj78offjwmWKL1SNVObR\ndXRLKN26rKGr4QQl/1bW2sQBKgtv6QKeveEU/NNhkphv/fZvxVnWMTSWrv2pv/oSAOA9jz2q/V01\nTI5bdhVOzstw5Fmp+eO0OpL/gGpKKTraano6uEjHVxbcv8pZYPRnkpHU9J/3yocRbW9mIo0pzBO9\nNk2zJ5YgVte1kvsE7UgLyx4TONXtXEDI6Ll2QVLd1Le9I6s4ez8T9Jw9BwDoLyyjkfRjzl4sjNbV\nKDhLksxZBP0d1A7FoJAXBY6ePIUsy/bKdenLnisXYGpKeKEMOfEGSVmtpNjyPEAiPBosMgFFwqsH\nUGpK6iGkYMVxunAygeY/JY03HI8xZJKQMQ8Kg04XR5Ypfbh2hKTkpswgbY1HU9NL4rsiGWZw4hS5\niBcuUsrw4vPnAQCdhRWcZRTi0oCCobnNNTW6OKDApIPHN30DsTd3F6h+4cWXCMvwtRcuYlyx5gAP\nuBs3buMYpzhfvHBe+09xBMw6JW44QtTDqJhA5NLlq3j3uyngmXEJ+s11Oue1a3dw6hilzQRhabMS\ngZdiNUv+dfIiqk7zbWmE4QkhPvCqYN3oINZwsdFkMkKX7+MCs19JUVtuk0FBiulUsjcOgtk+CFl5\nJuZferF55KPsN51ONdDY4/aQGy99y2nF4OF5ueBZDXz1GC0PHnrne7HKSwVYnjy8gefn2RppGxS3\nEQV80snvHtY+tNZaa//xmXmlEfCeNsKYmwCGAG693W0BcBRtO1Jr2zFr/yG344EQwrFX2+lQDAoA\nYIx5KoTwwbYdbTvadry97WiXD6211tqMtYNCa621NmOHaVD4xNvdALa2HbPWtmPW/qNvx6GJKbTW\nWmuHww6Tp9Baa60dAjsUg4Ix5ruNMc8ZY14wxvzMPTrnfcaYzxpjvmKM+bIx5u/z9lVjzB8bY57n\nz9dWjP7625MZYz5vjPl9/veDxpjPcZ/8pjHmtcn8vL42rBhjftsY81VjzLPGmA+9Hf1hjPkHfE+e\nMcb8ujGme6/6wxjzS8aYG8aYZ5Jt+/aBIfvfuE1fNMY88Ra34x/zvfmiMeZfGmNWku8+zu14zhjz\nXW/k3G/7oGBIF+KfAfgeAI8B+GFD+hFvtTUA/tsQwmMAngTwU3zenwHwmRDCowA+w/++F/b3ATyb\n/PsfAfinIYRHAGyABHbeavt5AH8QQngXgPdxe+5pfxhjzgD4aQAfZPGhDKQlcq/645PYq3PySn3w\nPSDKwUcB/CSAX3iL23Fv9FZCCG/r/wF8CMAfJv/+OICPvw3t+F0A3wngOQCneNspAM/dg3OfBT1s\nfwPA74NwqbcA5Pv10VvUhmUAL4PjTMn2e9ofIEmASwBWQTD83wfwXfeyPwCcA/DMq/UBgP8TwA/v\nt99b0Y657/4LAL/Gf8+8MwD+EMCHXu9533ZPAfEhEDuQVsSbacaYcwA+AOBzAE6EEK7xV9cBnLgH\nTfhfQUS4Uti7BmAzBNYWuzd98iCAmwB+mZcx/9wYM8A97o8QwhUA/wTARQDXAGwBeBr3vj9Se6U+\neDuf3b8L4F+9Fe04DIPC22rGmAUA/zeA/yaEsJ1+F2jYfUvTM8YY0el8+q08zwEsB/AEgF8IIXwA\nBDufWSrco/44AlIaexDAaQAD7HWj3za7F33wambegN7KQewwDAoH1op4s80YU4AGhF8LIXyKN68b\nY07x96cA3HiLm/FhAB8xxpwH8BugJcTPA1gxxkgV673ok8sALocQPsf//m3QIHGv++NvAng5hHAz\nhFAD+BSoj+51f6T2Sn1wz59dE/VWfoQHqDe9HYdhUPhLAI9ydLkEBUw+/Vaf1FBt6S8CeDaE8L8k\nX30awMf474+BYg1vmYUQPh5COBtCOAe69n8TQvgRAJ9F1Oi8F+24DuCSMeadvOk7QFT997Q/QMuG\nJ40xfb5H0o572h9z9kp98GkAP8pZiCcBbCXLjDfdzL3SW3krg0avIaDyvaBo6osAfvYenfNbQG7g\nFwF8gf//vaD1/GcAPA/gXwNYvYf98G0Afp//fohv7AsA/i8AnXtw/vcDeIr75HcAHHk7+gPA/wjg\nqwCeAfAvQBoj96Q/APw6KJZRg7ynn3ilPgAFhP8ZP7dfAmVM3sp2vACKHcjz+n8k+/8st+M5AN/z\nRs7dIhpba621GTsMy4fWWmvtEFk7KLTWWmsz1g4KrbXW2oy1g0JrrbU2Y+2g0Fprrc1YOyi01lpr\nM9YOCq211tqMtYNCa621NmP/Py0i44tmWpo4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa76b35710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat, _ = next(loader)\n",
    "print dat.shape\n",
    "plt.imshow(dat.numpy()[0].transpose((1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from /home/data/world-cities/spatial-maps/splits/012/ log scale\n",
      "Found 29850 images in subfolders of: /home/data/world-cities/spatial-maps/splits/012/\n"
     ]
    }
   ],
   "source": [
    "normalize_channels = ([0, 160.91, 1.295, 0, 0],\n",
    "                      [1, 1010.413, 15.326, 1, 1])\n",
    "normalize = True\n",
    "take_log = [1,2]\n",
    "rotate_angle = 0\n",
    "res_scale = 64\n",
    "\n",
    "loader = dl.get_loader(\"/home/data/world-cities/spatial-maps/splits/012/\", None, \n",
    "                       8, res_scale, \n",
    "                       load_attributes=None, #[\"region\", \"profiles\"], \n",
    "                       rotate_angle=rotate_angle, \n",
    "                       normalize=normalize, \n",
    "                       take_log=take_log,\n",
    "                       custom_loader=True)\n",
    "loader = iter(loader)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2\n"
     ]
    }
   ],
   "source": [
    "for i, data in enumerate(loader, 0):\n",
    "    print i, len(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean [-0.96100003  0.34599999 -0.49399999]\n",
      "Stdv [ 0.18799999  0.89700001  0.83200002]\n",
      "Min  [-1. -1. -1.]\n",
      "Max  [ 1.  1.  1.]\n",
      "bulgaria_blagoevgrad_bansko_very-small_11833_41\n",
      "direct: ['0.0000-255.0000', '0.0000-255.0000', '0.0000-255.0000']\n",
      "torch : ['-1.0000-1.0000', '-1.0000-1.0000', '-1.0000-1.0000']\n",
      "prepr: ['0.0000-1.0000', '0.0000-1.0000', '0.0000-1.0000']\n"
     ]
    },
    {
     "data": {
      "image/png": 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v9ntrPvob/Tv1LWA0ObsJmGJmtwGziKgIb9+63QGv1+7YXvc8NXd4arqBdId3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\nbeuvaDfeErBo8yIAjumWfUD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9c7caf210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fXuZafd771T637o+D189ly74u1yfGWFUqh4isBC4BngIW2g8GgJNo8/zw9q4M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\nxeHdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9c61da950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VDQaDYX+jqBWYhZW+Yad5bn5/Azu2dRHpjxGywqi73P3091cyd14lSilEhFBN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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa83317c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat, names = next(loader)\n",
    "\n",
    "mu_smpl = dat.numpy().mean((0,2,3))\n",
    "sd_smpl = dat.numpy().std((0,2,3))\n",
    "max_smpl= dat.numpy().max((0,2,3))\n",
    "min_smpl= dat.numpy().min((0,2,3))\n",
    "\n",
    "print \"Mean\", np.round(mu_smpl, 3)\n",
    "print \"Stdv\", np.round(sd_smpl, 3)\n",
    "print \"Min \", np.round(min_smpl, 3)\n",
    "print \"Max \", np.round(max_smpl, 3)\n",
    "\n",
    "i = 0\n",
    "\n",
    "# data processed through torchvision\n",
    "img = dat[i].numpy()\n",
    "\n",
    "# data read directly\n",
    "img_path = \"/home/data/world-cities/spatial-maps/splits/012/img/%s.png\" % names[i]\n",
    "img0 = imread(img_path)\n",
    "img1 = basic_preprocess(img0.copy(), res_scale, \n",
    "                        normalize=normalize, log=take_log)\n",
    "\n",
    "print names[i]\n",
    "print \"direct:\", [\"%2.4f-%2.4f\"%x for x in zip(img0.min((0,1)), img0.max((0,1)))]\n",
    "print \"torch :\", [\"%2.4f-%2.4f\"%x for x in zip(img.min((1,2)), img.max((1,2)))]\n",
    "print \"prepr:\", [\"%2.4f-%2.4f\"%x for x in zip(img1.min((0,1)), img1.max((0,1)))]\n",
    "\n",
    "prc = [\"direct\", \"torch\", \"proces\"]\n",
    "src = [\"bldg\", \"pop\", \"lum\"]# \"water\", \"bnds\"]\n",
    "\n",
    "for i,r in enumerate(src):\n",
    "    fig, ax = plt.subplots(1,3, figsize=(9,6))\n",
    "    ax[0].imshow(img0[...,i], cmap=cm.GnBu); ax[0].set_ylabel(r)\n",
    "    ax[1].imshow(img[i,...], cmap=cm.GnBu);\n",
    "    ax[2].imshow(img1[...,i], cmap=cm.GnBu)\n",
    "    for a,s in zip(ax,prc):\n",
    "        # a.axis(\"off\")\n",
    "        a.set_title(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3404428199680865"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = float(reduce(lambda x, y: x * y, [l for l in dat.size()], 1))\n",
    "dat.norm(1)/scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7028c359d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tuwEe4BLgL8DTWuuOwC5gZLR1GAyG5BNLKvrWwFygB7AbeB94DpgMtNBalyql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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70308e4110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img0[...,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f702898e9d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LJJsVjZ9pi+u1mk28hE5VZ5UlUyd0jIjfnc+xt6FlafVy2mEGYPvpulR8jEoY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\nU+JlntgM+7WE2HGPwHx5pGzRJOySh6LTpO2RZCrtulejDhs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AgMIFgoIiCUDXbBQNmnQn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7028a7ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(rotate_ndimage(np.expand_dims(img0[...,0], 2))[...,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.,  0.,  0.,  0.,  0.]),\n",
       " array([  1.00000000e+00,   1.54510000e+04,   6.15202191e+01,\n",
       "          1.00000000e+00,   1.00000000e+00]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img0_rot = rotate_ndimage(img0)\n",
    "img0_rot.min((0,1)), img0_rot.max((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.,  0.,  0.,  0.,  0.]),\n",
       " array([  1.00000000e+00,   1.54510000e+04,   6.15202191e+01,\n",
       "          1.00000000e+00,   1.00000000e+00]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img0.min((0,1)), img0.max((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 5, 64, 64])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([8, 64, 64])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.sum(torch.abs(dat), 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.27593567090662"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mean(torch.abs(dat[:,1,...]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6417.65990652889"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mean(torch.sum(torch.abs(dat[:,1,...]), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# identify images with no data on relevant channels and remove them\n",
    "mean_channels = dat.numpy().mean((2,3))\n",
    "\n",
    "idx_rm = []\n",
    "for i in range(mean_channels.shape[1]-1):\n",
    "    print \"Channel %d:\"%i, (mean_channels[:,i]==0).sum()\n",
    "    idx_rm += np.where((mean_channels[:,i]==0))[0].tolist()\n",
    "    \n",
    "# for i in idx_rm:\n",
    "#     f1 = \"/home/data/world-cities/spatial-maps/splits/train/%s.tif\"%names[i]\n",
    "#     f2 = \"/home/data/world-cities/spatial-maps/splits/train/%s.pickle.gz\"%names[i]\n",
    "#     os.remove(f1)\n",
    "#     os.remove(f2)\n",
    "\n",
    "# Channel 0: 3\n",
    "# Channel 1: 1\n",
    "# Channel 2: 16\n",
    "# Channel 3: 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Flip ndimage (equivalent of the flip functionality in torchvision but for numpy arrays istead of PIL images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = np.arange(12).reshape((2,2,3))\n",
    "print A\n",
    "flip_ndimage(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display/save ndimage by channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "ndarr = save_image_channels(dat, #filename=\"./bla.png\", \n",
    "                            ncol=8, padding=3, take_log=[1,2],\n",
    "                            scale_each=False, normalize=True,\n",
    "                   channel_names=[\"bldg\", 'pop', 'lum', 'water', 'bounds'],\n",
    "                   sample_names=[\"S%d\"%i for i in range(10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Torchvision Lambda Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "import torch\n",
    "from datafolder import basic_preprocess, default_loader, flip_ndimage, rotate_ndimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_path = \"/home/data/world-cities/spatial-maps/splits/train/%s.tif\" % names[4]\n",
    "img0 = imread(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transf_list = []\n",
    "use_channels = [0]\n",
    "transf_list+=[transforms.Lambda(lambda img: img[...,use_channels])]\n",
    "transf_list += [transforms.Lambda(lambda img: basic_preprocess(img,res_scale, log=take_log, normalize=normalize))]\n",
    "transf_list += [transforms.Lambda(lambda img: flip_ndimage(img,0)), \n",
    "                transforms.Lambda(lambda img: flip_ndimage(img,1))]\n",
    "transf_list += [transforms.Lambda(lambda img: rotate_ndimage(img,rotate_angle))]\n",
    "transf_list += [transforms.Lambda(lambda img: torch.from_numpy(img.transpose((2,0,1))).float())]\n",
    "transform = transforms.Compose(transf_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timg0 = transform(img0)\n",
    "plt.imshow(timg0.numpy().squeeze(), cmap=cm.gray_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute radial profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sys.path.append(\"/home/nbserver/urbanization-patterns/cityanalysis\")\n",
    "from cityanalysis import City\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cities = []\n",
    "for i in range(8):\n",
    "    M = dat[i,:3].numpy().transpose((1,2,0))\n",
    "    water = dat[i,3].numpy() == 0\n",
    "    bounds = dat[i,4].numpy()\n",
    "    cities += [City(M, name=\"S%d\"%i, \n",
    "                    mask=None, bounds=bounds, \n",
    "                    sources=[\"built\", \"pop\", \"lum\"])]    \n",
    "    \n",
    "colormap = {\"built\":\"black\", \"pop\":\"green\", \"lum\":\"orange\", \"all\":\"blue\"}\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "# using radial estimation method\n",
    "sns.set_context(\"notebook\", font_scale=1.3)\n",
    "fig, ax = plt.subplots(1,8, figsize=(16,2), \n",
    "                       gridspec_kw={\"wspace\":0.03, \"hspace\":0.25},\n",
    "                       sharex=True, sharey=True)\n",
    "\n",
    "\n",
    "for i,city in enumerate(cities):\n",
    "    profiles = city.compute_profile(method=\"radial\", step=2, scale=False)\n",
    "\n",
    "    L = len(profiles.values()[0][0])\n",
    "    xlabels = np.arange(L) * 0.75 * 4\n",
    "\n",
    "    for c,p in profiles.iteritems():\n",
    "        s = np.nanmax(p[0])\n",
    "        s = 1 if s == 0 else s\n",
    "        ax[i].plot(xlabels, p[0] / s, label=c, lw=2, color=colormap[c])\n",
    "    # ax[i].legend(loc=\"best\")\n",
    "    ax[i].set_xlabel(\"$d \\ [km]$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(dat[1,1].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(dat.size()[:2][::-1])\n",
    "ax = ax.flatten()\n",
    "\n",
    "for i in range(dat.size()[0]):\n",
    "    for c in range(dat.size()[1]):\n",
    "        plt.imshow(dat.numpy()[i,...].transpose((1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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